The Good Tech Companies - Rethinking Simulation: How CompLabs Is Building Foundation Models for Mechanical Design

Episode Date: May 29, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/rethinking-simulation-how-complabs-is-building-foundation-models-for-mechanical-design. CompLabs is reimagining mechanical simulation with AI models that cut runtimes from days to seconds—boosting design speed, creativity, and precision. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #mechanical-simulation-ai, #complabs, #3d-geometry-ai, #foundation-models-engineering, #ai-for-mechanical-design, #physics-based-ai, #simulation-optimization, #good-company, and more. This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com. CompLabs is building a foundation AI model for mechanical simulations, enabling engineers to run high-fidelity tests in seconds without meshing. By understanding 3D geometry and physics, their approach makes design faster, cheaper, and more goal-oriented—unlocking creativity and accelerating innovation across industries.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Rethinking Simulation. How Comp Labs is Building Foundation Models for Mechanical Design. By John Stoyan Journalist. Mechanical engineers need to simulate how their product will perform under various physical conditions. However, these simulations can take days to run and cost thousands of dollars. Comp Labs is developing an AI model that can run the min seconds and at negligible cost. Why simulation matters? Designing a product is fundamentally an optimization problem. And this optimization relies on iteratively testing and improving designs based on feedback.

The faster you can test, the faster you can learn, the faster you can optimize. It was for this reason that in the 1960s, numerical solvers were popularized to approximate real-world physical conditions. These algorithms enabled engineers to test products in silico, rather than through costly and time-consuming physical prototypes, but the complexity of designs and simulations kept increasing, causing the solvers to have exponentially longer runtimes," says Chinmay Srivastava, co-founder and CTO at Comp Labs. Further, these simulations require specialized expertise and often weeks' off-missing, describing the geometry to the computer, costing large organizations hundreds of millions per year. The high cost of each simulation results in slow iteration and forces engineers

to explore a restricted design space, leading to suboptimal products. What if AI could understand designs and physics? The impact of AI understanding language has been significant, but what if AI could also understand designs and physics surrogate models are being used by advanced engineering teams to speed up simulations. They are narrow ML models trained on a specific simulation, requiring significant data and ML expertise. However, they can't accurately model nonlinear physics, capture high fidelity, or maintain accuracy with changes to the geometry, material, or physics. Many teams experiment with surrogate models, but often invest significant effort for limited return. We're providing them a faster, more generalizable way to accelerate simulations," says Noah Evers,

co-founder and CEO at CompLabs. Rather than training highly specific single-purpose models, CompLabs ISDs have a general AI model that understands 3D geometries and how they're affected by physical conditions. Their model is pre-trained on a diverse corpus of 3D geometries and how they're affected by physical conditions. Their model is pre-trained on a diverse corpus of 3D geometries and physics data. This enables it to replicate the performance of a company's solver after fine-tuning on prior simulation runs. Users can then run high-fidelity simulations in seconds on new geometries, materials, and physical conditions. Because the model can intuitively understand geometries, there's no need for meshing.

For example, one large engineering company is planning to use the model Toevaluate material and coding combinations for complex thermal components, compressing months of work into hours. Looking forward, Comp Labs has raised $2.65 million in pre-seed funding from Alt Capital, Corey Levy and Joris Port, CEO of Rescale. The team is now working with engineering leaders across aerospace, automotive, and materials to accelerate their complex simulations. Mechanical design today is slow. Weeks of meshing, simplifying models to make them computationally tractable, and runtimes of days to months.

These delays break the flow of design, making it hard to stay in a creative mindset. Our goal is to pull mechanical engineers out of the weeds and empower them as designers. With an AI model that understands geometry and physics, engineers will describe what they want, a part that can withstand certain loads, stay within thermal limits, or reduce drag, and the system will generate optimized designs. Design will become more goal-oriented, fast, and creative. Thank you for listening to this Hacker Noon story, read by Artificial Intelligence. Visit HackerNoon.com to read, write, learn and publish.