Stars
Relax! Flux is the ML library that doesn't make you tensor
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equat…
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 a…
Automatically update function definitions in a running Julia session
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Extensible, Efficient Quantum Algorithm Design for Humans.
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
Grid-based approximation of partial differential equations in Julia
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learni…
Meta-package for data analysis in Julia, modeled after the R tidyverse.
An atmospheric model for research: friendly, interactive, extensible, and built for speed
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
A Julia package for fitting (statistical) mixed-effects models
What scientific programmers must know about CPUs and RAM to write fast code.
Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
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…
Surrogate modeling and optimization for scientific machine learning (SciML)
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Probabilistic Programming with Gaussian processes in Julia
An intuitive modeling interface for infinite-dimensional optimization problems.
Automatic Differentiation Library for Computational and Mathematical Engineering
A toolbox for satellite analysis written in julia language.