Physics Simulation Assistant

Overview

A university research lab studying fluid dynamics spent more time configuring and running simulations than analyzing results. Each simulation required manual parameter tuning, ran for days, and often produced results that required adjustment and re-running. We built an AI assistant that understands the physics, suggests parameter configurations, predicts outcomes before full runs, and iteratively optimizes toward target results.

The Challenge

Physics simulations have hundreds of interdependent parameters where small changes can produce dramatically different results. The AI needed genuine understanding of the underlying physics to make useful suggestions, not just blind optimization. It also needed to work with existing simulation software (OpenFOAM, COMSOL) rather than replacing them.

Our Approach

We built a surrogate model trained on the lab's historical simulation data. Given a parameter set, the surrogate model predicts the approximate outcome in seconds rather than days. The AI uses this to explore the parameter space efficiently, suggesting configurations likely to produce desired results. Once a promising configuration is found, the full simulation runs for verification. The assistant also explains its suggestions in physics terms, not just optimization metrics.

Key Features

• Parameter space exploration with surrogate model
• Outcome prediction before full simulation runs
• Automated parameter tuning toward target results
• Physics-aware suggestion explanations
• Integration with OpenFOAM and COMSOL
• Experiment tracking and comparison
• Publication-ready visualization generation

Results

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