Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

18.337 - Parallel Computing and Scientific Machine Learning

Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

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

Deep learning library for solving differential equations and more

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

18.S096 - Applications of Scientific Machine Learning

Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

Julia interface to Sundials, including a nonlinear solver (KINSOL), ODE's (CVODE and ARKODE), and DAE's (IDA) in a SciML scientific machine learning enabled manner

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML)

Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software

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

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

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

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

Benchmarks for scientific machine learning (SciML) software and differential equation solvers

Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

Surrogate modeling and optimization for scientific machine learning (SciML)

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

Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem

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

A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications

Arrays which also have a label for each element for easy scientific machine learning (SciML)

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

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

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

Arrays with arbitrarily nested named components.

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)