Daniel and Kelly’s Extraordinary Universe - Why do scientists do simulations?

Episode Date: August 31, 2023

Daniel and Jorge explain how simulation lets scientists answer new kinds of questions about the Universe.See omnystudio.com/listener for privacy information.

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Hey, am I speaking to the real Jorge today? Who else could it be? I don't know. It could be the simulated Jorge or an AI generated Jorge. Hmm, Jorge GPT. Yes. Chat Jorge. Would it make a difference?

That sounds like something the simulated Jorge would say. I kind of wish I had simulated Jorge's. then I might avoid a lot of mistakes I make. Or they can do all the work while I sleep in. Why do you think simulated Jorge's are less likely to make mistakes? No, I mean, they would do the mistakes, and then I would learn from them. That's the idea, right? That does sound useful, but I think you have to be careful about the Sim Jorge's organizing

and rising up against you. Oh, do you think they would form their own union? Or a revolt, do you mean? Yeah, either mutiny or fair wages, either one. Well, I could just pay them in simulated money, I guess. As long as they can use that to feed their simulated children, I bet they'd be happy. Oh, no, I definitely provide simulated benefits, too.

The whole simulated family gets a bonus. You're not a good employer, but you can simulate one. Hi, I'm Jorge, I'm a cartoonist, and the author of Oliver's Great Big Universe. Hi, I'm Daniel. I'm a particle physicist and a professor at UC Irvine, and I am constantly simulating crazy conditions. You mean in your life or in your work? Well, work is a big part of my life, but yeah, my job involves simulating collisions at very high energies all the time. And you also actually do them, right?

You actually collide things at the Large Hadron Collider. That's right. We both collide particles together in real life and we simulate what would happen if we collided particles under various different potential laws of the universe to see what might happen. Will the earth get gobbled up? Will it create a black hole that destroys the earth or not? Let's find out. Does that mean your whole career is a simulation?

Or your whole life? You know, our computers are not fast enough to keep up with reality. So while we generate lots and lots of simulated collisions, the real collider has generated more collisions than we could ever simulate. But how do you know, Daniel, that we're not in a simulation right now? Like, you might think you're doing experiments, but really, you're just inside of a video game somewhere. Well, I want to find the cheat codes then.

Well, I think if you had found them by now, you probably would have a noble price, right? I'm hoping smashing particles together gives me the cheat codes. And then that opens up the real boss level. Where I fight the simulated army of Jorge's. No, you fight the real Jorge. That's the real boss. But anyways, welcome to our podcast, Daniel and Jorge,

explain the universe, a production of iHeartRadio. In which we use our tiny little minds to try to understand the vast, We hope to build in your head a simulation of sorts, one that describes the way the real universe works out there. We hope to encode into your brain some laws of physics that will help you understand how the real universe out there is smashing and bashing to create our bonkers reality. That's right. The universe is doing all kinds of amazing and awe, sometimes insane things out there in reality. And so it's our job as humans and as scientists to understand what's going on and to ask questions. To probe into the true answers to why things are the way they are.

And the classical way that science does this is with theories and experiments, hypotheses and tests. You have an idea, you go and see out there in the universe if it works. You predict something happens and you go and check to see if it does. But the modern scientific method has a third way, which lives sort of uncomfortably between theory and experiment. Oh, why is it uncomfortable? Is it like uncomfortable awkward or uncomfortable, like physically uncomfortable? It's a little bit uncomfortable for those of us who specialize in that to know where we fit into the picture of science. Some people consider me an experimental physicist.

Some people are like, nah, he mostly runs simulation, so he's really a theorist. So you can be sort of like uncomfortably between two different communities if you do a lot of simulations. You should start your own simulated community. Can you be like a simulating physicist? A simulator, a simulatrist. That sounds not safe for work, actually. I don't know what you mean, but I'll take your word for it. You know, academic communities change pretty slowly.

And for example, in departments of physics, people tend to hire people that are like them. The experimentalists get to hire somebody. They want to hire somebody who's a blue-blooded experimentalist down to the core. So if you work at the intersection of fields, you do some experiments, you do some theory, maybe even do some computer science and machine learning, then you don't necessarily have a home. You don't have a tribe that's going to go to bat for you. get hired. So it's sort of about the sociology of science as a real practice. Well, I think that kind of makes sense, right? Like, why hire a simulating physicist when you can

just simulate one? Why go to all the trouble? You know, I think that's true. If we could simulate physicists, we could get a lot more done. But we're not quite there yet. Human physicists still have a little bit of an edge. Yeah, maybe till next week when Chad GPT catches up and starts doing physics. I mean, have you ever asked Chad GPT a physics question? You don't get physics out. That's for sure. But anyways, it is an interesting universe because I guess sometimes there are questions you can't just go out there and try for yourself in the universe, right? That's right.

Simulation has emerged in the past 50 years as an extraordinarily powerful tool. It's a little bit of experiment and a little bit of theory. And it lets us answer questions that we otherwise could not answer. It really is a completely new tool in the science tool belt. Although I would argue it's maybe one of the oldest tools in science. And so today on the podcast, we'll be asking the question. Why do scientists do simulations? Why do scientists do anything?

Other than the obvious, that simulations are so much fun. You get to build your own little universe. You are the creator and god of that simulated universe. Oh, boy. Is that the ultimate goal there to be gods? No. You know, you'll have to get a degree for that. You can just buy some Legos.

Oh, I've been doing that since I was a little kid. I just want more and more powerful simulations. You want more powerful Legos, smaller Legos. Jokes aside, there is a real sense of power when you create a simulated universe because you are deciding what the laws of physics are in that universe. What particles do they have? How do they interact? And then you get to see how it all plays out.

Yeah, I kind of get that. I mean, I write a lot. I create characters and I sort of build my own world. What's the difference? Yeah, you could think of fiction as simulated human interaction and lives, right? We're exploring what it would be like to be in those situations. Yeah, wait, did you just say your work is fiction? Simulations are definitely fiction.

Sometimes they align with reality, and one deep question is how well they align. What lessons you can learn from your simulated fiction that carry over into the real world. Interesting. So your research is science fiction, is what you're saying. You know, I've always argued that there's a strong connection between science and science fiction, right? One aspect of science is like, well, what are the laws? Could they be this? Could they be that?

There's an element of creativity and exploration there. Absolutely. So, yeah, I'm constantly creating science fiction universes and trying to see if they line up with ours. And then you wonder why the other physicists don't want to play with you. Fortunately, I got 10 years before I revealed all of these crazy instincts. That's the game. Yeah. Well, this is an interesting question.

And so as usual, we were wondering how many people out there had thought about why scientists do the things they do. And in particular, why they do simulations in their work. So thanks very much to everybody who answers these questions for this fun segment of the podcast, one of my favorites. If you'd like to join a team or just answer one or two questions, write to us to questions at danielanhorpe.com. So think about it for a second. If someone asks you why scientists do simulations, what would you? say? Well, using simulations, we can observe scenarios in our models that we can't necessarily observe in real life and see what can happen in certain situations like in a black hole

or when galaxies collide or something like that. Scientists do simulations because the universe is really old, really big, sometimes really destructive. Frankly, I'm happy they do a lot of that modeling and simulations and don't necessarily try to create big bang conditions on a big scale or the explosion of stars or something. All right. A couple of interesting answers. Did anyone look at you funny when you asked them the question? I don't know.

These were all on the internet, so I couldn't capture their facial expressions. Did they send an emoji? But I do sense some relief in there. For example, we are trying to simulate galaxy collisions rather than trying to arrange. galaxy collisions. Well, if we could do that, that would be pretty cool. I mean, not for those galaxies, but just to have that power. Yeah, you'd have to have, like, sign-offs from every alien civilization in both galaxies before

you could even begin. I guess that would be the polite thing to do, yes. But anyways, as you were saying, this is a big part of how science is done these days. And so, Daniel, I guess let's start from the basics. What is a simulation in your view as a physicist? So, a simulation, or more specifically, a computer simulation, is a special. specific program that involves a scientific model. A model is like our picture of how the world might work. It's like a simplified version of the real universe, one that lets us explore a specific

question. And a simulation is usually a program on a computer that uses like step by step methods to explore the behavior of that model. And usually this model has a form of an equation, right? Like for example, F equals MA is a model of the world, right? And how things move. move in the world. Yeah, the science we do is mathematical and the way we describe things is mathematical. And so usually that involves equations, equations that represent constraints on the model, like the way things have to happen. And as you say, F equals MA is a model. I want to toss a baseball across my backyard. And I want to answer the question, where is it going to land? Then I have lots of possible ways to answer that question, but the most appropriate way is to

make the simplest model possible that still captures everything that I'm interested in. And so often in our world, like when we're tossing baseballs, we can do something pretty simple, just F equals MA, which ignores all sorts of swarming quantum details about what's happening inside the baseball and just describes simple motion of a parabola. Yeah, it's almost like you, I mean, as a scientist, you're trying to come out with the rules of the universe, right? That's sort of the goal of science, right? And what sometimes that rule looks like is an equation that says, you know, if you have a mass and you apply a force to it, then it's going to be. start moving with a certain acceleration exactly in your words these are all science fictions we're living in this world and we're wondering what are the rules and so we're trying a bunch of different rules saying does this rule describe our universe does that rule describe our

universe so every sort of theoretical exploration of the universe involves building a model and then asking the question does that model align with the reality that we see computer simulations are a special kind of model or special with a test really complicated models that we can't otherwise test. Like the model F equals M8, pretty simple. I can use pencil and paper to make predictions and then I can throw a ball in my backyard to confirm those predictions. Right, because I guess F equals MA has like a mathematical solution. But I think the idea is that you take an equation like F equals MA and you basically program that into a computer and say, you know, any masses in this program, they have to move according to this law. That's right.

If for example, I don't just want to describe one ball, but I want to describe like 10 to the 25 balls, All right, some huge number of balls. Maybe I'm modeling an ideal gas or like a swimming pool full of ping pong balls or something. And I want to describe that. Then I can no longer use pencil and paper. But you're right. I can take those equations and put them into a computer and ask the computer to force those balls to follow that equation. And then I could see what happens.

It's sort of like a virtual experiment. Yeah. It's like you're creating your own little universe, right? Exactly. And this becomes super essential when we don't have like a single equation that describes everything. Like we don't have a solution to what happens when you put 10 to the 25 ping pong balls into a swimming pool. We just don't know how to do that calculation to come up with some like nice summary of the results. But what we can do is put it into a computer and have the computer

step it forward in time very carefully and we can see what happens without ever actually having to buy that number of ping pong balls. Right. That's sort of the power of the computer, right? Like you can simulate one ball with just a calculator, right? Like you can say after one second, it's going to be here. After two seconds, it's going to be here by following these rules. But if you have, like you said, a whole bunch of balls or a more complicated system, then a computer can sort of do all those calculations for you faster. Exactly. One huge advantage is tackling a very large number of objects. And the other is when we don't have the equations. We don't know how to solve them. Like for F equals MA, we know how to solve that. Technically, that is a very large number of objects.

differential equation because A is a second derivative of position, right? There's derivatives on both sides. And in general in mathematics, differential equations are very, very hard to solve. There's a small number that we actually know how to solve. So sometimes you have a system that's described by differential equation and we don't know how to solve. Like fluid flow, for example, described by the Navier-Stokes equation, we don't know how to solve that in general. But what we can do on a computer is approximated. We can say, you know, let's just move it forward in time, not a year or a minute or some long period of time, but just like a microsecond. And across a microsecond, we can make some approximations.

We can say, let's not use the full equation. Let's simplify it and take some like linear approximation of it. And then if we take a lot of tiny little steps, we hope that we roughly get the right answer. Right, because I think as you were saying, like something like F equals to me has the solution, meaning that you can derive a formula for like the position of your ball at all times, where you can just like after three seconds, you just put the time in and it gives you the position of the ball, right? Because you can integrate that equation and find the solution. But some equations you can, like they're so complex. You can get a formula that will tell you

what's going to happen 10 years from now or 20 years from now, right? Those you need to do little steps by little steps. Exactly. And the crucial idea there is that you're making a linear approximation. You're taking the full equation, which you don't know how to solve, and you're saying, well, let's replace it with an approximate version of it, which is not going to be correct, but it might be correct for like a microsecond. And so we'll use the approximate linear version of that that that we do know how to solve for a tiny little step and then we'll start again and we'll make another tiny little step and we hope the little mistakes cancel out and don't build up into some big overall mistake right yeah it's it's almost like if you take small enough steps then you're less likely to

deviate from the reality of it right yeah exactly and people who do approximations know that there's lots of times this is useful like you want to calculate trig function like sign sign is really hard to calculate like sort of from scratch but for very small values of the angle sign of x is just equal to x you can like approximate this complicated function with a simple one and it mostly gets the right answer that's just one example i'm not sure going to trigonometry uh usually makes things you to understand but i think i think we get the idea which is that you know if you take small and no steps and you um you sort of a simplified version of your model then you're less likely to make mistakes exactly you can't trust those approximations forward a second or a minute or a year

but you can trust them like a micro second and so you have the simulated universe in your computer, you feed in the initial conditions, and then you ask it to take a step forward in time. And you ask it to take another step forward. And if you have enough computing power, you can run it for a while and you can see, hmm, what happens to all my ping pong balls in my simulated swimming pool? Or what happens to my galaxy as the stars all swirl around each other? Right. Like you were saying, like fluids are notoriously really hard to solve as an equation, right? These are really complex equations that govern what's going on because they sort of like depend on a lot of things like there's a lot going on right there's time and then

there's distance and the velocity of things and all of those factor in that's hard or impossible to like predict exactly what's going to happen in the future exactly and the big complication there is the interactions like back to the ping pong balls if you just had a lot of ping pong balls and they were all flying around but not touching each other it wouldn't be that hard to calculate what's going to happen to each one but as soon as they start banging against each other becomes much, much more complicated because the solution that ping pong ball number 642 now depends on ping pong ball number 1,111 and every other ping pong ball. So it becomes much more complicated.

And that's why fluids are so complicated because every like sheet of the fluid depends on the friction with the other sheet of the fluid. And that's what makes the Navier-Stokes equation, for example, so intractable. Right. And so you take it little by little. And so you say, okay, at this time, I'm going to ignore some of these effects and just take one small step to see where all those little molecules go and then you keep repeating that and hopefully it sort of looks like the real thing.

Exactly. And it gives you this incredible power that you can hopefully identify emergent behavior. The way we do science in our universe is that we like focus on one level where we understand things. We can describe like the microphysics of how particles being against each other. But sometimes we're interested in things at another level. Like you understand how raindrops move through the wind, but your real question is like, is this hurricane going to hit Florida or Alabama? And so even if you don't have like an

equation that describes hurricanes, if you have an equation that describes the raindrops, you can feed that all into your computer, run simulations, and then get answers to your higher level question. You can see like the emergent phenomena of the hurricane in simulation. Well, let's dig a little bit deeper into how simulation works and things like weather and why scientists use simulations to try to learn things about the real universe. But first of it, let's take a quick break. I had this, like, overwhelming sensation that I had to call it right then. And I just hit call, said, you know, hey, I'm Jacob Schick.

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from teachers or I get students who would be like it's easier to punch someone in the face. When you think about emotion regulation, like you're not going to choose an adapted strategy which is more effortful to use unless you think there's a good outcome as a result of it if it's going to be beneficial to you. Because it's easy to say like go you go blank yourself, right? It's easy. It's easy to just drink the extra beer. It's easy to ignore to suppress seeing a colleague who's bothering you and just like walk the other way. Avoidance is easier. ignoring is easier, denial is easier, drinking is easier, yelling, screaming is easy. Complex problem solving, meditating, you know, takes effort.

Listen to the psychology podcast on the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts. right? We're not really having a podcast, right? We're just pretending to have a podcast. We're simulating the process of injecting ideas into listener minds. And so we're talking about why scientists use simulations. And it's kind of a, I guess, a philosophical question, perhaps, because doing a simulation of reality is not really reality, right? And so you're not really experimenting on reality. So it's kind of a, I guess, a funny thing for scientists to do it because you're not really doing experiments on the real world, but it's at the same time really helpful, right?

That's true, but that same criticism could be applied to basically everything in science. When we do science, we never use all of the full, gory details of the universe to answer a question. We're always using some stripped down version because otherwise it's totally intractable. Like when we do F equals MA, even for a single ball flying through the air, we're ignoring lots of stuff. We're ignoring air resistance. We're ignoring quantum effects of the particles inside of it. We're ignoring all sorts of things because we don't think that they are important. And so every time you build a model of the universe theoretical or simulation,

you're always making a choice about what to ignore and what to include. Well, we talked a lot about what a simulation is, right? It's a computer program where you program in the rules that you think that the world follows, the rules of the universe, at least in your simulated universe, and then you sort of let the computer kind of run this world, and then it sort of tells you what may or may will sort of happen. Exactly. And it lets you examine all sorts of universes you don't otherwise have.

access to. Like in my work, it lets me answer questions like, what would I see in our particle detectors if the Higgs boson was this kind of particle? Or what if there was no Higgs boson? Or what if it had twice the mass that it had? What would we see in our detectors? What would that universe be like? What would those experiments result in? So it gives you ideas for experiments or it can guide your real experiments, right? That's part of the idea, right? It's actually crucial for interpreting our experiments. When we look at data from the actual collider and we see these splashes of energy here and splashes of energy there and we look at the patterns, the correlations, the way we interpret those is by comparing them to simulations. We say, hmm, is this consistent with the Higgs boson

with these properties or is it more consistent with the Higgs boson with some other properties? So the simulation in some sense defines the ideas that we're considering the various hypotheses that we're trying to distinguish between. Right. It lets you explore the possibilities. That's the idea of a simulation, right? Let's you maybe make mistakes. Absolutely. And before we build a detector, we simulate it to see like, hmm, is this going to work or how well is it going to perform? Or oops, turns out we need to swap the order of these two things or nothing's going to work. So, yeah, making mistakes in simulation is much cheaper than baking them in reality.

Yeah. And as you were saying, simulations play a big part in weather prediction, right? I mean, that's how weather predictions work. Like when you look at the weather forecast and says it's going to rain tomorrow, it's because some big computer out there has basically taking the data from today and simulated what's going to happen tomorrow. Exactly. And that's really the origin of computer simulations. People wanted to predict the weather to understand what's going to happen to these cloud patterns.

But nobody could really do it with pencil and papers too complicated, too many pieces of information and the equations are really just a mess. So meteorology is one of the first places where people decided, let's code this up on the computer to try to grapple with this complexity and see if we can get anything right. That was just after World War II when computers were first displaying like computational power. And it was the weather forecasters and the nuclear physicists that first really jumped on this train. Yeah, because I think the way the weather works is that you have all this data, meteorological data, weather data across, let's say, the United States that tells you the wind speeds and the clouds and the pressures and all that.

And then you can use that and put it into basically your computer, which has a model of what should happen next, if that's the, you have all these, these pressures and wind, wind patterns. Exactly. And those models are not perfect. They don't describe everything. And so they're most reliable over short times because that's when the errors are not going to compound as much, which is why like the prediction for how hot it's going to be tomorrow is much more reliable than the prediction for how hot it's going to be in 10 years,

which you basically have no information about. Or if you look at those projections for like, where's the hurricane going to be? The potential path of the hurricane gets wider as the prediction gets, further out because there's more uncertainty, like, is it going to hit Alabama? We don't know. Yeah, it's pretty cool. And actually, an interesting fact, I would just talk to a hurricane scientist a couple months ago. And he was saying that we're still at the point, apparently, where humans outperform simulations, even supercomputer simulations. Humans using pencil and paper or just humans like with their intuition? Humans with their intuition. So like, apparently we're

still at the point where if you have, if you're seeing a hurricane move, you run a computer simulation about where it's going to go next, a human or like a season experience hurricane watcher will still, today, better at predicting what the hurricane is going to do just from what's going on inside their brain and the history of what they've seen before. But it's getting apparently closer and closer. So maybe in the near future computers will make those hurricane watchers totally obsolete. That's super fascinating. And it's fun to think about like what's going on inside that person's brain. They have built in their head some neural network with literal biological neurons, right, not your typical artificial neural network that models hurricanes

and they've trained it on a bunch of real hurricanes. So that's pretty cool. Yeah, it makes me wonder if maybe in the future they're going to use AIs to predict the weather. Maybe you don't need a scientific model of what's going on. That's a really fascinating question because AIs are already being used to help boost simulations. One problem with simulations is that they can be very expensive computationally. You have lots and lots of raindrops and you want to model it very very accurately. It takes a computer a long time to calculate every rain job and move it forward in time. You want to predict something a few days out, it can be very expensive computationally. We run into this problem in particle physics all the time because we want to simulate

billions and billions of potential collisions and the interactions with the detector are very complicated. So to generate one simulated collision, for example, takes like 30 minutes, even on a modern computer. We use AI to boost those to make them faster. Essentially, we train machine learning algorithms to reproduce what the careful calculations have done. They don't understand it. They don't like have the same equations built in. It's just sort of like those people watching the examples and getting an intuition. This is like a machine learning intuition.

So now you're not just working in a made-of-world. Now you're intuiting your way through a made-of-world. Yeah. And one problem is that we don't always know if their predictions are accurate or why they make them. You can't ask them like, hmm, why did this go left instead of? of right. They just have an internal model. The same way your hurricane watchers probably can't answer detailed questions about why they feel it's going this way. They just feel it. Yeah. And so it also raises these interesting philosophical questions about what science is, right?

Like, is it still science if you get an AI to predict what's going on, even if you don't understand what the AI did? It's a deep question that we're struggling with all the time. But what's not controversial is that it gives us extraordinary power to do things we just couldn't do otherwise. We can now run our simulations for much, much longer and in much more depth. You want to know what's going to happen when the Milky Way collides with Andromeda or the far future of our universe, simulations give you that power. You don't have to sit around and wait for the events to play out. We can test it in simulation.

Right. That's pretty cool. And so what are the different types of simulations that scientists use? I would say that simulation is almost everywhere in science. You know, it used to be limited to a few computationally complex fields, but now everybody sees. how useful it is. You know, even big companies, like you want to design a new airplane and you're considering a few different wing shapes.

It used to be that you had to build prototypes of those wing shapes and put them in a real huge wind tunnel, very time-consuming and expensive. Now you can just simulate the wind tunnel and get an idea for which wing shape is going to work. You can explore thousands of different shapes simultaneously. So that can be very, very powerful. So I think simulations are essentially everywhere in science now. Well, I think they've been using simulations in things like aerospace for a long time, right?

Like even I'm thinking in the space program in the 50s and 60s, I mean, they didn't use physical computers, but they use people computers to sort of simulate what the trajectories of the spacecrafts were going to be, right? They definitely used human brains to do those calculations. Whether you consider that a simulation, I think, is a tricky point. Is that just a theoretical calculation, which people have been doing, you know, since Galileo or Francis Bacon or whatever? Or is it actually a simulation? I don't know. That's a tough question.

All right. So then what are some of the other types of simulations people do? Or what are some other ways that physicists use simulations? Another way they use them is to observe things that they otherwise couldn't see. Like we want to know what's going on inside the sun. Well, we have really no prospects for actually seeing what's going on inside the sun. But we can build a simulation of the inside of the sun. And that's going to make predictions for things that we can see, things happening on the surface.

of the sun or the number of neutrinos coming to Earth. And that helps us get an understanding for what's really happening inside the sun. And in the simulation, you're not limited, right? You can ask questions about anything that's happening. Like, what is the temperature at the core of the sun? What is the velocity of the plasma? So often simulations always checked by real experiments in places where we can observe them, give us access to things that we can't otherwise observe.

I think that's a crucial step in this process, right? Like you can come up with this imaginary world in your computer, but it has to match sort of what you see at the end with reality, right? Absolutely. Otherwise, it's just science fiction, which, you know, has its own value, but there's a special interest in our universe. And that's led to all sorts of deep understanding. You know, the original simulations of the sun predicted a huge number of neutrinos landing on the surface of the Earth, and they went out and measured them, and the answer was

wrong. And they thought, hmm, did we get the sun wrong? Or is there something going on with neutrinos? And it turned out the simulation of the sun was correct. And neutrinos were doing something wonky between there and, here. Right. I think the idea is that if you create a simulation and you tweak the parameters of it, right, like the numbers in it so that it matches what you see coming, for example, out of the real sun, then the idea is that maybe what you think is going on inside the sun is

actually what is going on inside the sun. Yeah, that's exactly right. And somebody else might come up with another simulation saying, actually, I think something else is happening. And then you can ask, well, what's the difference between these two simulations? Do they predict any different things that we actually can measure, and then you can go off and use that to distinguish between two various ideas. And we talk about this all the time on the podcast. Sometimes we have like two different possible ideas for what's happening near black holes. Remember, we once talked about the magnetic field near black holes, something we could definitely not measure. And there were two different models. One was called mad, one was called sane. And they made slightly different predictions.

And then the recent picture of the black hole helped us distinguish between these two models, these two simulations for black hole magnetic fields. Right. But I guess that's the tricky thing. It's like just because the simulation matches what you see at the end, it may not necessarily be what's going on inside, right? It could just be sort of a coincidence that it matches. It certainly could be, and you always have to be careful trusting your simulation.

You always need ways to validate it and to ensure that the bits that are important to your science question are accurate. Because I guess sometimes that's the only option that we have, right? like you're saying you can't just stick a stick inside the sun to see what's going on and things like maybe black holes or the big bang like there's no way where we can go back in time and do an experiment on the big bang right so we sort of have to rely on these simulations to try to understand what was going on yeah and they've turned out to be extraordinarily powerful tools that give us insight into what might have happened in the early universe or what's going

on to the hearts of black holes or neutron stars i can't really imagine doing science without them All right. Well, that's sort of, I guess, a pretty good answer for why scientists use simulations and surprise twist. This whole conversation was just a simulation of our discussion of the topic. This is like a sixth sense. I only see simulated people. This was not the real podcast, right, Daniel? That's right. Yeah. And hopefully this answer is also true in the real universe. But Daniel, you got to interview a scientist who does physics and actually also wrote a book about simulating things in the universe. That's right. I had a fun chat with Professor Andrew Ponson. He's a cosmologist and a professor at University of College London, and he wrote a new fun book called Universe in a Box, which explores the role of simulation in cosmology and in science in general. And he does have an answer for the question, is our universe a simulation? Now, if I order a universe in a box, do I get a universe in a box? Maybe you get a recipe for how to put a universe into a box. That's not the universe in a box. And also, why a box?

Why not, I don't know, a spherical container? I thought you were going to ask, if the universe is in a box, what universe is the box in? No, I wasn't going to ask that. Dang, well, my simulated Jorge went arrive in. It's in the multiverse. I don't know. Aren't they like meta universes outside of our universe? Isn't that the idea?

Click on the multiverse box option on Amazon shipping. The question then is, can you? you have a multiverse in a box. Anyways, you had a great conversation with Andrew. I certainly did. What motivated him to write this book? He felt like the role of simulation in science was super important, and yet hadn't really been explored by any pop-side book,

and so he wanted to share his love for simulations with everybody. Cool. Well, here is Daniel's interview with Professor Andrew Ponson about his new book, Universe in a Box. So then it's my pleasure to welcome to the program, Andrew Ponson. a cosmologist and professor at University College London. Andrew, thank you very much for joining us today.

Oh, thank you. So your book is called Universe in a Box. It's a fascinating and compelling history and sort of definition of what is a simulation and why it's important in science. So let's start off with the very, very basics. What is a simulation in your point of view? There are different definitions you can give,

but I think a good place to start is by thinking it's trying to capture some, element of the real world inside a computer and that can take many different forms it doesn't even have to be a physics right it's we can have simulations of something like human behavior there are simulations of the way that crowds might behave that architects use to make safer buildings by making the passageways the right kind of size and shape that if there's an emergency situation then humans will evacuate the building in the most efficient safe way but I think what that already teaches you is that it's possible

to do a simulation of something without necessarily understanding everything about that thing before you start. Because if you think of crowds, they're made out of people, we can't actually predict everything about how an individual human is going to

react in any given scenario. And yet, we can make simulations that are useful. They might not be perfect, but they're useful for giving us some insight into the way that crowds might behave. So when you take that across to the physics environment and in particular, my area, cosmology, it's about trying to capture something about how the universe behaves,

but we know from the outset we're never going to get that perfect. So then where is the value in a simulation? If you have to like encode in already what's going to happen when people bump into each other or the purchasing choices of people or how galaxies interact, if you have to already build in the physics, what are you learning from that? the simulation. How do you get any information out of it? Well, the point is that you code in some things about how the individual bits within your simulation behave. I guess, you know, in the case of the crowd, that would be how an individual human might behave under a variety of circumstances.

But in the case of physics, it might be how we think dark matter particles flow through the universe and interact with each other through gravity. And what the simulation does is take a very large number of those elements and kind of have them all individually doing their thing, but the behavior that then emerges can be very hard to anticipate in advance. And that's the point. Understanding how a crowd behaves is not at all the same thing as understanding how a human behaves. And in the same way, understanding how dark matter behaves through our whole cosmos is not at all the same thing as understanding what an individual particle of dark matter might do. This principle of emergent phenomena is something I'm super fascinated by.

It's incredible to me that sometimes we have laws of physics at one scale, which, you know, causally determine different sort of laws of physics at another scale, which we can't always easily predict, but as you say, we can observe in action if we can, you know, construct the right setup. To you, is simulation, is it a kind of experiment? Is it a kind of theory? Or is it sort of a new branch of science? I think it's a new branch, but I think it's got something in common with theory and with experiments. And I guess I tilt mainly towards thinking of it as an experiment. Now, that's a little bit controversial. Sometimes people say, well, it can't be an experiment. You've told the computer what to do, whereas in an experiment, you're supposed to go and ask

nature. You know, you're supposed to put things to the test and confront them with the reality of how things really work. But, you know, I'm not sure that that does. distinction is always so clear. So an example that I give in the book is, let's say you're just trying to build an aircraft and you have some idea about how you want to shape the wing, but you don't know exactly how that wing is going to perform. Now, you now have a choice. You could build a scale model of your wing and put it inside a wind tunnel and see how it performs inside a wind tunnel, and that's kind of an experiment. Or you could make a digital version of your wing and put it inside an airflow inside a computer

that's a kind of simulated airflow and see how it behaves there. And both of those are going to have limitations. There's definitely limitations on what you can achieve inside the computer. But there's also limitations in what you can achieve in a wind tunnel. You can't make an infinitely big wind tunnel. It's going to have edges. Things are going to be the wrong scale.

So when you do experiments, you are making some set of assumptions about the real world and how what you're doing applies to the real world. And I think that's true in simulations as well. So overall, this is why I start to think more and more of simulations as

types of experiment. I have an argument with my brother who's a computer scientist and he runs what he calls experiments on his machine learning models. I'm like, that's not an experiment. You're just doing it in your computer. But you're absolutely right.

If you don't know the outcome and you're learning something, it can be considered also. an experiment. But you mentioned something which I wanted to ask you about anyway, which is the limitation of simulation. You're concocting sort of an artificial universe and you're learning something about that universe. If that universe doesn't follow the same rules as ours, then obviously we're not learning something about reality, which usually is the goal. Can you say something about how we know when to trust our simulations, what the fundamental limitations of simulation are?

Yeah, I mean, that is the hardest, most difficult question at the heart of doing good simulation, is knowing what to trust and what not to trust, and often it's hard. You know, it can be really hard to know. I mean, fundamentally, the limitation is just computational power that even if you're doing something like a weather forecast, which I talk about a bit in the book, you know, just the atmosphere of the earth has so many molecules in it that you're never going to track each individual molecule, right? So you're going to going to have to make some kind of approximation, you're going to parcel up the air into almost like big hypothetical bags of air that move around through our atmosphere and use some laws to describe that. But then you're going to have to go and say, well, you know, we're not getting all the small scale details right. We're going to have to put in some corrections in the case of meteorology. Even clouds can be quite hard to predict because you're just not getting all of those tiny details that contribute to the way

that a cloud forms in reality. So you need to go in and put into your simulation some kind of correction almost by hand. You say under these circumstances clouds must start to form. And, you know, if you're a weather forecaster, you see

how well did I do by making that assumption about how clouds form and over time you sort of incrementally improve by comparing how your simulation did with the reality of how the weather unfolded. So we can do something a bit similar in cosmology. It's not quite the same because we don't get to kind of do the repeat experiments

in quite the same way as you do in weather forecasting, for example. In some level, it's the same kind of iterative process that we're getting better over time. We're understanding the way that we have to put in corrections to our simulations to account for things like the way that stars evolve and change over time and dump energy into the universe and what black holes are up to and all of these things that we actually have to help the computer along the way, if you like. So why is it that we need to make these corrections, that we have these limitations?

Is it purely just computational power? And in the limit of infinite computing power, could we predict the weather tomorrow starting from particle physics and modeling every single cork in the atmosphere? Or is there a conceptual limit there, some obstacle that we can't overcome even with infinite computing. I think both are a problem. So first of all, we are very far from having infinite computer power, a very, very long way away from that. But secondly, you're right. I mean, there are more fundamental limitations as well. In particular, we do not know exactly where every molecule is in the atmosphere to start with. So even if you had a computer powerful enough

to track at the molecular level what our atmosphere is doing, you wouldn't know how to start the simulation. You wouldn't have enough data to tell it what the atmosphere looks like today. So there's an inaccuracy that's sort of just coming from not having that perfect data. And an effect known as chaos means that imperfect initial data very quickly turns into big errors. So there's a kind of famous example of this. It was Edward Lorenz who sort of gave the thought experiment of a butterfly flapping its wings somewhere in Europe, say, and it has a sort of series of knock-on effects that over time just amplify and amplify and eventually the tiny little gust from the butterfly's wings actually stimulates the formation of a hurricane.

And, you know, these kind of effects, we know they're there in physics. We call them chaos. And so, you know, the slightest inaccuracy in how you set things up, will eventually make the simulation depart from reality. So we know that's true in cosmology as well. And we don't have perfect information about the early universe. We have quite good ideas for what was going on there. But it's imperfect, and that means because of chaos and the way that those imperfections are amplified over time,

what we end up with is in some sense it's like a statistical recreation of the universe. It's telling us statistically what sorts of things should be in the universe, and what sorts of mixtures and what kind of patterns, rather than literally recreating the universe. So it's almost more like sort of climate. It's like a climate simulation almost rather than a weather simulation. I'm very interested in the history of simulations as well. I mean, theoretical science is like thousands of years old,

experimental science people argue about might be hundreds of years old. Simulation-based science seems like decades old. Can you take us back to the root of it? Where does it begin, really? Well, I think, you know, the very earliest route you can find is in the 19th century where Charles Babbage and Ada Lovelace were working on the idea of a computer very similar to our modern computers. It was the first time, really, anybody expressed the idea of having a machine

that could be told to perform any calculation. So before that, there were machines that did specific calculations. but this was the first time somebody envisaged a machine where you could just give it instructions and it would carry out calculations to your specifications. And Ada Lovelace actually wrote at that time that one of the applications of being able to do that was to be able to take what we think are the governing laws of physics

and make them kind of practical, get the computer to do all of the calculations that turns those abstract equations, into concrete-specific predictions for different scenarios. So that's probably the first time anyone expressed what we would recognize as a modern simulation. Then in terms of actually performing simulations, remarkably some people tried to do this in the 20th century

before digital computers were actually made. So there are some beautiful stories like a crazy character called Lewis Frye Richardson, who was actually on the front line of World War I, trying to calculate weather forecasts using pen and paper. Very, very repetitive calculations he was doing. That would be exactly what a computer does today to do a weather forecast. But he was doing it just with pen and paper, taking him weeks, stretching out into years, just to do one forecast.

He wasn't trying to be practical about it. He was just trying to prove a point. that this is actually doable in practice. And then, you know, by the end of World War II, there were actual computers available, and very quickly from there, the whole business of simulating all sorts of different things, but ultimately the entire universe,

it kind of grew up quite quickly from there. I had this, like, overwhelming sensation that I had to call it right then. And I just hit call, said, you know, hey, I'm Jacob Schick, I'm the CEO of one child, Foundation and I just wanted to call on and let her know there's a lot of people battling some of the very same things you're battling and there is help out there. The Good Stuff Podcast Season 2 takes a deep look into One Tribe Foundation, a non-profit fighting suicide in the veteran community. September is National Suicide Prevention Month, so join host Jacob and Ashley Schick as they bring you to the front lines of One Tribe's mission.

I was married to a combat army veteran and he actually took his own life to suicide. One Tribe saved my life twice. There's a lot of love that flows through this place and it's sincere. Now it's a personal mission. I don't have to go to any more funerals, you know. I got blown up on a React mission. I ended up having amputation below the knee of my right leg and a traumatic brain injury because I landed on my head.

Welcome to Season 2 of the Good Stuff. Listen to the Good Stuff podcast on the Iheart radio app, Apple Podcast, or wherever you get your podcast. Hola, it's Honey German. And my podcast, Grasasas Come Again, is back. This season, we're going even deeper into the world of music. and entertainment with raw and honest conversations with some of your favorite Latin artists and celebrities. You didn't have to audition? No, I didn't audition. I haven't audition in like over 25 years. Oh, wow. That's a real G-talk right there. Oh, yeah. We've got some of the biggest

actors, musicians, content creators, and culture shifters sharing their real stories of failure and success. You were destined to be a start. We talk all about what's viral and trending with a little bit of chisement, a lot of laughs, and those amazing vivras you've come to expect. And, of course, we'll explore deeper topics dealing with identity, struggles, and all the issues affecting our Latin community. You feel like you get a little whitewash

because you have to do the code switching? I won't say whitewash, because at the end of the day, you know, I'm me. But the whole pretending and code, you know, it takes a toll on you. Listen to the new season of Grasasas Come Again as part of My Cultura Podcast Network on the IHartRadio app,

Apple Podcast, or wherever you get your podcast. A foot washed up a shoe with some bones in it. They had no idea who it was. Most everything was burned up pretty good from the fire that not a whole lot was salvageable. These are the coldest of cold cases, but everything is about to change. Every case that is a cold case that has DNA. Right now in a backlog will be identified in our lifetime. A small lab in Texas is cracking the code on DNA.

Using new scientific tools, they're finding clues in evidence so tiny, you might just miss it. He never thought he was going to get caught, and I just looked at my computer screen. I was just like, ah, gotcha. On America's Crime Lab, we'll learn about victims and survivors, and you'll meet the team behind the scenes at Othrum, the Houston Lab that takes on the most hopeless cases

to finally solve the unsolvable. Listen to America's Crime Lab on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts. I'm Dr. Scott Barry Kaufman, host of the psychology podcast. Here's a clip from an upcoming conversation about exploring human potential. I was going to schools to try to teach kids these skills, and I get eye rolling from teachers or I get students who would be like, it's easier to punch someone in the face. When you think about emotion regulation, like you're not going to choose an adaptive strategy

which is more effortful to use unless you think there's a good outcome as a result of it, if it's going to be beneficial to you. Because it's easy to say, like, go blank yourself, right? It's easy. It's easy to just drink the extra beer. It's easy to ignore, to suppress, seeing a colleague who's bothering you and just, like, walk the other way. Avoidance is easier. Ignoring is easier.

Denial is easier. Drinking is easier. Yelling, screaming is easy. Complex problem solving, meditating, you know, takes effort. Listen to the psychology podcast on the Iheart Radio app, Apple Podcasts, or wherever you get your podcast. Tell us more about the role of simulation in your personal research. Is this something you explore because you're fascinated by the computer science of it? Or to use it just a tool that helps answer your physics questions?

I think it depends on the day you ask me. I mean, some days I really enjoy the computer science of all this. And, you know, it's undeniably cool to get to work with some of the. the world's biggest supercomputers and be able to instruct them to carry out these kind of simulations. And the results are enormous fun to work with as well. So there is a bit of that kind of nerdery in it. But I think ultimately the thing that really keeps me hooked is the idea that we are contributing to a bigger picture of how our universe evolved, of the role for materials in it that we

as yet don't understand things like dark matter and dark energy, that we know they're out there. They seem to be having a profound effect on our universe, but we really don't know what they are. And we're trying to learn more about that. And then I suppose ultimately, what we're building towards is a better understanding of where we came from. You know, the existence of us, carbon-based life forms on this rocky planet is part of the story that we're telling, because the chemical elements from which our planet and life are constructed weren't there in the Big Bang. They've been manufactured over time.

They need very specific conditions to be manufactured and then concentrated enough to start forming planets and enabling life and so on. So I think ultimately that's the thing I'm most excited by that we are telling this bigger story that in the end speaks to a kind of deep question within all of us about where did we come from?

Have you seen yet machine learning being used to amplify or speed up or just overall enhance simulations in your research? Yeah, I mean, machine learning is more and more important throughout astrophysics. So it's being used in a variety of different ways. One is to try and improve on some of these things that we were talking about a moment ago, about, you know, what do you do about the things that you can't quite get right, like the way that stars form out of gas, it's just such a complicated process

that we can't capture it perfectly, the way that those stars then put energy back into the galaxy that they're forming within, the roles of black holes, all of these things that are very complicated and multifaceted. We can use machine learning to do some of the hard work for us to learn from examples of individual simulations,

stars say about how they behave and kind of learn the lessons from those and then take them and put them in the bigger setting of trying to simulate then hundreds of billions of stars across a galaxy or maybe even out into the universe. So machine learning can kind of help us take lessons from one bit of our simulations or one bit of physics and insert them in an efficient way into other simulations. That's one role for them. But they're also crucial in interpreting the data we get from the real universe. So the data that is coming from telescopes and especially big new survey telescopes, things like Euclid and the Vera Rubin Observatory,

these giant efforts to scan the sky and build maps of our universe, they need machine learning because the machine learning can kind of do a lot of the initial data processing, figuring out what's interesting, what we're actually looking at, where it is in 3D space, and what needs to be flagged for further human follow-up. All of these things,

machine learning is playing an increasingly important role. And in your view, what is the future hold for simulation-based science? Are there fields of science that don't yet use any simulation and are on the cusp of being revolutionized by this powerful new tool? I mean, I'm not aware of fields that have shied away from simulation.

I think it is such a powerful tool that when you start looking for it, you do find it in use absolutely everywhere. But I think what we can say about simulation is that we're still a very early stage of understanding how to use simulation and what roles it can play within the overall scientific progress. So, you know, it goes all the way back to what really is a simulation. Is it an experiment or is it a calculation? How should we think about it? And tied up with that is this question of how can we improve our simulations?

How do we get past that stage of, well, we just have to kind of play around with things and tweak them until they fit because of the intrinsic limitations? So I think, you know, we're at a very early stage in understanding all of these things. So for certain the role that simulations play is going to change and evolve, and I hope improve over the coming years. So if you use the words universe and simulation together in a sentence, it of course evokes in people's minds this conversation that seems to be omnipresent, which is whether or not our universe could be a simulation or the same question.

When we build artificial universes that have bits and pieces in it, could those universes feel real to those occupants? So in your book, you take a sort of skeptical view of the question of whether we could be living in a simulation. Since you're an expert on simulating universes, what are your arguments against the concept that we could be living in a simulation? Yeah, I think the primary argument is just to look at the complexity of the universe that we're in. So you can actually calculate something called the number of qubits. So basically the number of quantum bits that you would need in a quantum computer if you wanted to do, according to all the physics we know so far, if you wanted to do a

perfect simulation of our universe. And that is a vast number. I forget it off the top of my head. I think it's something like 10 to the 124 cubits, something like that. It's a very, very large number. And right now, you know, we struggle even to make a single cubit in a quantum computer. It's a vast extrapolation from where we are now to the idea that we will routinely be able to run simulations that have the same kind of richness as the reality. that we currently live in. But even more than that, if you ask, well, you know,

what resources would you need to build a computer that really had that level of capability? Well, it turns out you would need to make use of the entire universe just to simulate one universe because physics puts limitations on information processing.

You can't just process as much information as you like. There are limitations placed on it. according to the physical system that it's being processed by. And so these come back to bite you. And that's what gives rise to this claim, that you can only simulate the whole universe perfectly if you have access to the entire physical resources of that universe. Now, there are lots of further objections you can raise.

You can say, well, what about, for example, if the higher-up universe where we're being simulated is just a much bigger universe with, many more cubits at their disposal, and so these resources seem terribly trivial maybe to the people in the higher-up universe. But at that point, I kind of lose patience with the argument, because it seems to me at that point it's no longer a sort of obvious extrapolation from where we are now. You're now talking about hypothesizing beings with so much more power and so much more technological capability than we have, that we might as well just go and talk about

religion, because at this point, it's lost contact, in my view, with any science. Wonderful. Well, I really enjoyed your book. And a question I always have for folks who write popular science books about very technical topics is why. What do you think that the general public folks out there who are not simulation experts need to know about simulation? I think there were two reasons. The first was, it was amazing to me that nobody had written about this topic before because it's so central in our field of cosmology. And, you know, cosmology is something that people do talk about a lot. There's a lot of it out there in the media and in books because it's genuinely, you know, it's exciting and I think it speaks to all of us about where we came from. And so it seemed

a real surprise to me that this central tool in how we're learning about the universe was not being written about. And so it felt to me like it needs to be rectified. We need to be talking about this because it offers immense strengths, you know, real enormous strengths that I think are kind of hidden away sometimes. But it also comes with lots of caveats, some of them we've discussed, you know, that we can't do these perfect recreations of the universe. There are lots of approximations. We might be making mistakes and, you know, there's no way to rule that out. It's just part of the scientific process that we have to keep an open mind about these things. So I wanted to write about that in a kind of open and honest way rather than sort of leaving the simulations as black boxes that seemingly

recreate our universe through some process of magic. No, it's a very human process. And it's got all of the strengths and weaknesses that come with being that kind of human process. Wonderful. Well, thank you very much again, Professor Andrew Ponson at University of College. London, an author of the book The Universe in a Box out now. I encourage everyone to go out and read about how science is actually done. Thanks again very much, Andrew, for joining us today. All right. Did you feel you had a real conversation with him? Like, did things get real? Or was it all just simulated pleasantries? I was able to simulate enjoying a conversation, yeah. Oh! That's sort of always my situation, though, because I'm kind of an introvert, so I have to

simulate enjoying human interactions. You have to simulate being engaging human being. I'm trying to quietly slip unnoticed into human society. So you sort of are a simulation, Daniel. I'm simulating being a human. But anyways, that was a great conversation.

What's your main takeaway from it? To me, it's fascinating that so recently in the history of science, just the last few decades, we've developed this crucial new tool that we now think is indispensable and makes me wonder in 50 years and 100 years

what new branch of science or what new tool we're going to add to our toolbox, which future scientists will think is indispensable and we'll wonder like, how did Daniel and folks who lived way back then do any science without it? Or maybe even like, why did Daniel do science? Why didn't they choose to use the AI physicist to answer all the questions? Yeah, stay in bed and let the AIs do the work. The answer to that is the AIs will answer questions the AIs are interested in. And science is for people, by people, and of people. So you've got to have people asking. questions. Wait, wait, do you mean the AIs won't always do what we ask them to do?

They'll always do exactly what we ask them to do, just not maybe in the way that we want them to. All right. Well, stay tuned to see if we are in a simulation right now, maybe, I guess. Some people consider the whole universe to be server a simulation, right? How can we possibly know? Yeah, like, what's the difference? If we are in a vast alien simulation, then I definitely want to talk to those aliens because they have some awesome computers. And also stay tuned. But we'll what new developments humans are going to discover using simulations and or artificial intelligence. So we hope you enjoyed that. Thanks for joining us.

See you next time. Thanks for listening. And remember that Daniel and Jorge Explain the Universe is a production of IHeartRadio. For more podcasts from IHeartRadio, visit the IHeartRadio app, Apple Podcasts, or wherever you listen to your favorite show. News. Why are TSA rules so confusing? You got a hood of you. I'm gonna take it all.

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Go off on me. I deserve it. You know, lock him up. Listen to No Such Thing on the IHeart Radio app, Apple Podcasts, or wherever you get your podcast. No such thing. Every case that is a cold case that has DNA. Right now in a backlog will be identified in our lifetime.

On the new podcast, America's Crime Lab, every case has a story to tell. And the DNA holds the truth. He never thought he was going to get caught. And I just looked at my computer screen. And I was just like, ah, got you. This technology's already solving so many cases. Listen to America's Crime Lab on the IHeart Radio app, Apple Podcasts, or wherever you get your podcasts.

Hi, it's Honey German, and I'm back with season two of my podcast. Grazacios, come again. We got you when it comes to the latest in music and entertainment with interviews with some of your favorite Latin artists and celebrities. You didn't have to audition? No, I didn't audition. I haven't auditioned in like over 25 years. Oh, wow. That's a real G-talk right there.

Oh, yeah. We'll talk about all that's viral and trending with a little bit of cheesement and a whole lot of laughs. And, of course, the great Vibras you've come to expect. Listen to the new season of Dresses Come Again on the I-Heart Radio app,

Apple podcast, or wherever you get your podcast. This is an I-Heart podcast.