Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Learning function operators with neural networks.

Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

Π-ML: Learn data-driven similarity theories of physical problems

Supporting code for "reduced order modeling using advection-aware autoencoders"

Deep learning for Engineers - Physics Informed Deep Learning

Deep learning library for solving differential equations and more

MeshfreeFlowNet: Physical Constrained Space Time Super-Resolution

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

Hidden Fluid Mechanics