Fixing the world one invalidator at a time.

Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)

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Importers for the BaseModelica standard into the Julia ModelingToolkit ecosystem

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

A structure interface for SciML to give queryable properties from user data and parameters

Faster `findfirst(==(val), dense_vector)`.

Rewrite Inplace Operations to be OOP if needed

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A SciML symbolic-numeric compiler tool for investigating functions in order to perform optimizations

GPU accelerated Particle Swarm Optimization

Basically just a surrogate in disguise

Implementation of the finite volume method in 1D.

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Common types and interface for discretizers of ModelingToolkit PDESystems.

Simpler stacktraces for the Julia Programming Language

Repository for automatic differentiation 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

A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages

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.

Solver for two-dimensional conservation equations using the finite volume method in Julia.

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

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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.

Generation of reaction networks from PubChem data

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