Turning equations into trainable neural networks automatically

Hybrid scientific machine learning typically requires either hand-coding physics equations or letting neural networks reinvent known laws from scratch. The Neural Compiler translates symbolic equations (written in a Scheme-like language) directly into differentiable PyTorch modules that match floating-point precision. In experiments across Feynman equations, pendulums, and PDEs, compiled modules recovered physical constants to under 1% error with minimal trainable parameters, while standard neural baselines needed thousands of parameters and achieved 7–93% error. The real win: equations compose without error accumulation, and the interface enables LLMs to generate executable hybrid models from natural language descriptions.