pytorch-physics

What problem does it solve?

This Skill helps you model and learn physical systems governed by differential equations, turning scarce or noisy observations into solutions constrained by physics.

Core Features & Use Cases

• Physics-Informed Neural Networks (PINNs): Enforce PDE residuals and boundary/initial conditions using automatic differentiation in PyTorch.
• Neural ODEs: Learn continuous-time dynamics from irregular time-series data and forecast future states.
• Data-Driven Force-Field Learning: Learn potential energy and derive conservative forces (e.g., energy conservation via F = -∇E).
• Use Case: Infer unknown PDE coefficients from noisy measurements (e.g., identify α in a diffusion/heat equation) and validate against known analytical or baseline solutions.

Quick Start

Use the pytorch-physics skill to train a PINN to solve a heat equation and compare the learned solution against the analytical ground truth using PyTorch autograd.