There are 24 repositories under scientific-machine-learning topic.
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.
An 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
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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.
Mathematical 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.
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and 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.
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
Surrogate modeling and optimization for 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.
18.S096 - Applications of Scientific Machine Learning
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)
Arrays with arbitrarily nested named components.
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
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
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
Reservoir computing utilities for scientific machine learning (SciML)
A style guide for stylish Julia developers
A library for Koopman Neural Operator with Pytorch.
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)