scientific-machine-learning
There are 27 repositories under scientific-machine-learning topic.
SciML / DifferentialEquations.jl
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
Language:Julia 2826- Language:Python 2558
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
Language:HTML 1834An acausal 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
Language:Julia 1401Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Language:Julia 951Pre-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
neural-odeneural-sdeneural-pdeneural-ddeneural-differential-equationsstiff-odeordinary-differential-equationsstochastic-differential-equationsdelay-differential-equationspartial-differential-equationsneural-networksdifferentialequationsdifferential-equationsscientific-mlscientific-aineural-jump-diffusionsneural-sdesscientific-machine-learningpinnphysics-informed-learningLanguage:Julia 855Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Language:CSS 712Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Language:Julia 705High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Language:Julia 527Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Language:Python 518Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
Language:Julia 454Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Language:Julia 403JuDFTteam / best-of-atomistic-machine-learning
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
atomistic-machine-learningawesome-listbest-of-listcomputational-chemistrycomputational-materials-sciencecondensed-matterdensity-functional-theorydrug-discoveryelectronic-structureinteratomic-potentialsmaterials-discoverymaterials-informaticsmolecular-dynamicsscientific-computingsurrogate-modelsscientific-machine-learningquantum-chemistryai4scienceA 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.
Language:Julia 329Surrogate modeling and optimization for scientific machine learning (SciML)
Language:Julia 328Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Language:MATLAB 318The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Language:Julia 306mitmath / 18S096SciML
18.S096 - Applications of Scientific Machine Learning
Language:HTML 304- ComponentArrays.jl
jonniedie / ComponentArrays.jl
Arrays with arbitrarily nested named components.
Language:Julia 286 Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Language:Julia 285GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Language:Julia 279Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Language:Julia 270DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Language:Julia 259Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Language:Julia 242LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
Language:Julia 238High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Language:Julia 225A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
Language:Julia 224ChrisRackauckas / universal_differential_equations
Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
Language:Julia 217Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
Language:Julia 212Koopman-Laboratory / KoopmanLab
A library for Koopman Neural Operator with Pytorch.
Language:Python 210Julia 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
Language:Julia 208A style guide for stylish Julia developers
Language:Julia 207- ReservoirComputing.jl
Reservoir computing utilities for scientific machine learning (SciML)
Language:Julia 206 idrl-lab / idrlnet
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.
Language:Python 193Solving differential equations in R using DifferentialEquations.jl and the SciML Scientific Machine Learning ecosystem
Language:R 139Build 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)
Language:Julia 138
