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muladdmacro.jl

This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem

multiscalearrays.jl

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

nbodysimulator.jl

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

neurallyapunov.jl

A library for searching for neural Lyapunov functions in Julia.

neuraloperators.jl

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

neuralpde.jl

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

nonlinearsolve.jl

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

nonlinearsolvebase.jl

nonlinearsolveminpack.jl

Wrappers for MINPACK into the SciML Common Interface

ode.jl

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.

odeinterfacediffeq.jl

Adds the common API onto ODEInterface classic Fortran methods for the SciML Scientific Machine Learning organization

operatorlearning.jl

No need to train, he's a smooth operator

optimalcontrol.jl

A component of the SciML scientific machine learning ecosystem for optimal control

optimization.jl

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.

optimizationbase.jl

The base package for Optimization.jl, containing the structs and basic functions for it.

ordinarydiffeq.jl

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

ordinarydiffeqextendedtests.jl

Extended tests for the OrdinaryDiffEq.jl library

parallelbroadcastarrays.jl

Wrappers for arrays to make broadcasted operations multithreaded and multiprocessed for high-performance scientific machine learning (SciML)

parameterizedfunctions.jl

A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications

pdebase.jl

Common types and interface for discretizers of ModelingToolkit PDESystems.

pderoadmap

A repository for the discussion of PDE tooling for scientific machine learning (SciML) and physics-informed machine learning

pdesystemlibrary.jl

A library of systems of partial differential equations, as defined with ModelingToolkit.jl in Julia

poissonrandom.jl

Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)

polychaos.jl

A Julia package to construct orthogonal polynomials, their quadrature rules, and use it with polynomial chaos expansions.

preallocationtools.jl

Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes

psogpu.jl

GPU accelerated Particle Swarm Optimization

pubchem.jl

pubchemreactions.jl

Generation of reaction networks from PubChem data

pydstool.jl

A wrapper for the Python PyDSTool library for the SciML Scientific Machine Learning organization

quantumnldiffeq.jl