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

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

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

Closed-form Continuous-time Neural Networks

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

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

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

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.

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

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

18.S096 - Applications of Scientific Machine Learning

The SciML Scientific Machine Learning Software Organization Website

Arrays with arbitrarily nested named components.

Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)

Boundary value problem (BVP) solvers for scientific machine learning (SciML)