Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more.

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

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

Brian is a free, open source simulator for spiking neural networks.

18.337 - Parallel Computing and Scientific Machine Learning

🌊 Numerically solving and backpropagating through the wave equation

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

Astus' Mathematical Display Application : A GUI for Mathematics (Calculator, LaTeX Converter, Plotter, ... )

High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

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

scikit-fmm is a Python extension module which implements the fast marching method.

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

Fortran Object-Oriented Differential-equations Integration Environment, FOODIE

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

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

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

R package for statistical inference using partially observed Markov processes

Animations of random double pendulums

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