Christopher Rackauckas

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 and automated transformations of differential equations

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

Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

A bunch of helper functions for transferring data between worker processes

Create a Site from Jupyter Notebooks

A set of templates for scientific career building resources.

Studying quadrature methods applied to PINNs

Familiar vectorized routines from R/Python/MATLAB, plus some more.

This is a detailed example for how to make a Julia package

A repo equivalent to a giant hug!

This is an example Julia package!

SciML Methods for turning TCAD models into SPICE models

Course material for the autumn school in scienfic machine learning 2023

An Introduction to Julia and DifferentialEquations.jl

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Mean-Independent Noise Control of Cell Fates via Intermediate States

Collection of papers and references for scientific machine learning techniques and applications

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Julia package for Schur decomposition of matrices with generic element types

Intel MKL linear algebra backend for Julia

JuliaCon 2018 PDE Workshop

JuliaCon 2018 PODIA.jl Talk

Efficient unit-checked computation

Educational material and tutorials for the Turing language

Julia version of the COPSE (Carbon, Oxygen, Phosphorus, Sulphur and Evolution) biogeochemical model.

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

An opinionated code formatter for Julia. Plot twist - the opinion is your own.

The official registry of general Julia packages

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

Physical quantities with arbitrary units

A Julia library of positivity-preserving time integration methods

The Julia Language: A fresh approach to technical computing.

NumFOCUS Google Summer of Code Materials

Interface between ControlSystems and ModelingToolkit

Interactive data visualizations and plotting in Julia

A cookbook of epidemiological models

Forward Mode Automatic Differentiation for Julia

ParameterEstimation.jl is a Julia package for estimating parameters and initial conditions of ODE models given measurement data.

Idea for a website for interactive solving of numerical differential equations

Julia Project web site.

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A framework for composing and simulating multiphysics systems

Auto QUality Assurance for Julia packages

DiffEq solvers for ordinary differential equations

Tools to generate and study moment equations for any chemical reaction network using various moment closure approximations

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Black-box optimization for Julia

Distributed High-Performance symbolic regression in Julia

Run testitems in parallel.

A parser combinator library for Julia

Making packages work faster with more extensive precompilation

Multivariate polynomials interface

Symbolic expressions, rewriting and simplification

Project Preferences Package

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Generation of reaction networks from PubChem data

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Workshop materials for training in scientific computing and scientific machine learning

Implementation of the finite volume method in 1D.

Developer documentation for the SciML scientific machine learning ecosystem's differential equation solvers

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

Network analysis algorithms for reaction networks modeled using Catalyst.jl

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

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Differential equation solvers with global error estimation

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

Finds relationships between the parameters of a mathematical model

Wrappers for calling the R deSolve differential equation solvers from Julia for scientific machine learning (SciML)

Wrappers for GeometricIntegrators.jl into the SciML common interface for scientific machine learning (SciML)

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

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High level functions for analyzing the output of simulations

Common interface bindings for the MATLAB ODE solvers via MATLAB.jl for the SciML Scientific Machine Learning ecosystem

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

A library for building differential equations arising from physical problems for physics-informed and scientific machine learning (SciML)

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

High dimensional numbers and reductions recipes for data visualization of scientific machine learning (SciML)

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

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

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Reservoir computing utilities for scientific machine learning (SciML)

A scientific machine learning (SciML) wrapper for the FEniCS Finite Element library in the Julia programming language

Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)

Line Search Algorithms

Static types useful for dispatch and generated functions.

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

Solves stiff differential algebraic equations (DAE) using variable stepsize backwards finite difference formula (BDF) in the SciML scientific machine learning organization

Interface to DASKR, a differential algebraic system solver for the SciML scientific machine learning ecosystem

A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)

A common solve function for scientific machine learning (SciML) and beyond

Differential equation problem specifications and scientific machine learning for common financial models

A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)

A collection of functionality around rooted trees to generate order conditions for Runge-Kutta methods in Julia for differential equations and scientific machine learning (SciML)

GPU accelerated Particle Swarm Optimization

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Finite State Projection algorithms for chemical reaction networks

Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning organization

Julia interface to CasADi via PythonCall

Julia wrapper for Pyomo that interfaces with Symbolics

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Common types and interface for discretizers of ModelingToolkit PDESystems.

A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools

A faster approximation to floating point power, at the trade-off of some accuracy

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

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

Toolbox to handle and solve split formulations of a wide variety of ODE and DAE problems.

Delay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

Fast uncertainty quantification for scientific machine learning (SciML) and differential equations

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Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

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

Functions generated at runtime without world-age issues or overhead

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

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

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

Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices

A standard library of components to model the world and beyond

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

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

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

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Boundary value problem (BVP) solvers for scientific machine learning (SciML)

A library of data interpolation and smoothing functions

Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)

Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.

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

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

Fixing the world one invalidator at a time.

Scheduler for Polyester.jl and compatible libraries such as LoopVectorization.jl.

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, SDEs, DDEs, DAEs, etc.

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

The Base interface of the SciML ecosystem

Collection of builder repositories for BinaryBuilder.jl

A high-performance, extensible flight dynamics framework for Julia

A bunch of functions which are helpful for maintaining large Github organizations

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

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.

A data-driven model fitting framework

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GitHub action to downgrade julia compat entries before testing

Flexible interactive visualization for large trees in Julia using D3.js

A benchmarking framework for the Julia language

(yet another) static site generator. Simple, fast, maths with KaTeX, code evaluation, LaTeX-like commands, optional pre-rendering, in Julia.

An optimized graphs package for the Julia programming language

Bijection datatype for Julia.

GitHub action for JuliaFormatter.jl

Direct solution of large sparse systems of linear algebraic equations in pure Julia

An experimental code analyzer for Julia. No need for additional type annotations.

Symbolics-based function property propagation for optimization

We make it rain like crazy.

Tools for the exploration of chaos and nonlinear dynamics

Arrays with arbitrarily nested named components.

Basic numerical integration routines for presampled data.

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Embed Julia in R

A Julia package for computing a (fine) mixed subdivision and the mixed volume of lattice polytopes

Extensions for Julia's docsystem.

Update immutable data

Package to help connect Julia with the SymPy library of Python

Core structures and tools for ODINN.jl

Global glacier model using Universal Differential Equations for climate-glacier interactions

Fast and flexible glacier ice flow models

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Trajectory Propagators for Astrodynamics.jl

A Julia style guide that lives in a blue world

A Julia/JuMP Package for Power Network Optimization

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A component library based on ModelingToolkit

High Oscillatory Ordinary Differential Equation Solver in Julia

Implementation of SIAN in Julia

Repository for SciML AD backend types

Julia implementation of elliptical slice sampling.

Named dimensions and indexing for julia arrays and other data

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Solver for 1D nonlinear partial differential equations in Julia based on the collocation method of Skeel and Berzins

Marginal distributions and Markov kernels that play nice with each other for the purpose of Bayesian state estimation.

A Julia library of summation-by-parts (SBP) operators used in finite difference, Fourier pseudospectral, continuous Galerkin, and discontinuous Galerkin methods to get provably stable semidiscretizations, paying special attention to boundary conditions.

Repository for materials related to program milestone hackathon and evaluation events

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Build Petri net models compositionally

Basic color definitions and traits

Call R from Julia

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Documentation of PSS/E and PSCAD Benchmarks for AGM Project

Distributed Julia arrays using the MPI protocol

Chris Rackauckas's Scientific Machine Learning Github Profile

Config files for my GitHub profile.

Wrapper for cminpack multivariate root finding routines

Julia bindings for the Enzyme automatic differentiator

adaptive 1d numerical Gauss–Kronrod integration in Julia

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Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing

Update deeply nested immutable structs.

Cunning tricks though the julia compiler internals