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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…
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
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 i…
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learni…
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Julia bindings to the FFTW library for fast Fourier transforms
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
A Julia/JuMP-based Global Optimization Solver for Non-convex Programs
Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains
A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)