pytorch-physics

Solve PDEs with physics-informed neural networks by enforcing residuals and boundary conditions using PyTorch autograd. This Skill trains models constrained by governing equations to find forward solutions from noisy or scarce data.

Learn continuous-time dynamics from irregular time-series data using Neural ODEs to forecast future states. This Skill applies continuous-time dynamics learning to model and predict complex temporal evolution.

Force-field learning derives conservative forces by estimating potential energy and computing its gradient. This Skill uses PyTorch autograd to calculate forces like F = -∇E for data-driven simulation tasks.

Identify unknown PDE coefficients from noisy measurements by running inverse parameter identification. This Skill trains physics-informed models to infer parameters like diffusion coefficients and validate them against analytical solutions.

You need DeepXDE and torchdiffeq to perform scientific machine learning tasks as they provide essential physics DL tooling compatible with PyTorch. These dependencies support training loops for PINNs and Neural ODEs.

PyTorch autograd computes first and second order derivatives required for inverse problems and PDE residuals. This Skill leverages automatic differentiation to enforce governing equations and identify unknown physical parameters.