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

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations

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.

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

18.S096 - Applications of Scientific Machine Learning

Arrays with arbitrarily nested named components.

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

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

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

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

A parallel ODE solver for PyTorch

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

Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support

Neural Graph Differential Equations (Neural GDEs)

Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"