Physics

AI and machine learning applications in physics — from physics-informed neural networks to simulation.

Overview

Physics and ML intersect in several key areas:

• Physics-Informed ML — Neural networks that respect physical laws
• Simulation Acceleration — ML surrogate models for simulations
• Quantum ML — ML for quantum systems
• Scientific Discovery — Finding physical laws from data

Quick Start

Coming Soon

We’re actively developing resources for:

• Physics-Informed Neural Networks (PINNs) — Incorporating physical constraints into neural networks
• Simulation Surrogates — ML models that accelerate physics simulations
• Symbolic Regression — Discovering equations from data

Want to contribute? If you have resources to share for physics-focused AI/ML, we’d love to include them.