SE3HamDL

Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

Closed-form Continuous-time Neural Networks

Code accompanying my blog post: So, what is a physics-informed neural network?

PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

18.S096 - Applications of Scientific Machine Learning

A Physics Informed Neural Network made using PyTorch

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem