Common types and interface for discretizers of ModelingToolkit PDESystems.

Simpler stacktraces for the Julia Programming Language

Repository for SciML AD backend types

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

High level functions for analyzing the output of simulations

Workshop materials for training in scientific computing and scientific machine learning

-

Wrappers for MINPACK into the SciML Common Interface

Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

Functions to run the SBML Test Suite with SBMLToolkit, create logs and create reports for the SBML Test Suite Database

High-level model-order reduction to automate the acceleration of large-scale simulations

SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia

-

-

Automatic Finite Difference PDE solving with Julia SciML

A standard library of components to model the world and beyond

Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.

Solving difference equations with DifferenceEquations.jl and the SciML ecosystem.

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

A deprecated way of handling discrete data in continuous equations

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

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.

No need to train, he's a smooth operator

SBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit

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

A Julia package that breaks down the curse of dimensionality in solving PDEs.

Julia MathML parser

Static types useful for dispatch and generated functions.

Nonlinear solver bindings for the SciML Interface

The Base interface of the SciML ecosystem

Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia

Differential equation solvers with global error estimation

Functions generated at runtime without world-age issues or overhead

Under some conditions you may need this function

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

Fast and automatic structural identifiability software for ODE systems

CellMLToolkit.jl is a Julia library that connects CellML models to the Scientific Julia ecosystem.

Helicopter Scientific Machine Learning (SciML) Challenge Problem

Finds relationships between the parameters of a mathematical model

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.