Control-of-Stochastic-Quantum-Dynamics-with-Differentiable-Programming

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

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

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

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

Code for the paper "Learning Differential Equations that are Easy to Solve"

Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

Neural Graph Differential Equations (Neural GDEs)