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AI Physics Engine to replace simulations and prototypes
What if every experiment you wanted to run could be modeled using AI? Godela is an AI-powered physics engine that gives engineers faster, cheaper replacement to simulations and physical prototypes. No set up, no wait. Just ask—and get simulation quality result.
Godela provides AI-powered physics intelligence that learns the laws of the physical world to predict and optimize real-world systems. It enables users to train physics-constrained models from their data, query what-if scenarios, and optimize configurations with solver-level accuracy in seconds.
Godela trains physics-constrained models from a user’s data (e.g., simulations and measurements), then supports rapid inference to answer what-if questions (changing airflow, geometry, materials) and optimize configurations to achieve desired outcomes. The workflow includes uploading data, training a physics-aware model, performing exploratory queries, and running optimizations that evaluate thousands of configurations to converge on optimal behavior, with results provided in seconds instead of days.
Who it’s for: Engineers and researchers who design or operate physical systems that involve thermals, electronics cooling, aerodynamics, electromagnetics, and structural analysis; teams needing fast, physics-accurate predictions from existing data without building new hardware.
YC backing mentioned; mentions pilot program and team with academia connections, indicating early traction and fundraising signals
Built laptops and iPads at Apple. Built AI products for Google. Now building Godela—AI that models the physical world, for engineers and scientists everywhere.
Researched physics-driven optimization at Harvard and generative models for fluid mechanics at Stanford. Built surrogate models for semiconductor manufacturing at Intel. Now building Godela - giving engineers and scientists their own AI model for physics.
Bringing AI powered intelligence to the physical world
Godela launches an AI-powered physics engine that turns engineering questions into computable, physics-informed models. It accepts natural language or data, supports CAD and existing results, and provides instant, simulation-quality insights to accelerate design and iteration for engineers.
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