Monte Carlo simulation routines for high-performance parallelization of differential equation solvers and scientific machine learning

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

Library for common tools for solving PDEs with finite difference methods (FDM), finite volume methods (FVM), finite element methods (FEM), and psuedospectral methods in a way that integrates with the SciML Scientific Mechine Learning ecosystem

Under some conditions you may need this function

Wrappers for MINPACK into the SciML Common Interface

Nonlinear solver bindings for the SciML Interface

Generation of reaction networks from PubChem data

No need to train, he's a smooth operator

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

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

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

Simple differential equation solvers in native Julia for scientific machine learning (SciML)

A stack implementation with a reset! function which avoids garbage collection for scientific machine learning (SciML)

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

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

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

Finds relationships between the parameters of a mathematical model

Julia MathML parser

Solving difference equations with DifferenceEquations.jl and the SciML 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)

Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Implementation of the finite volume method in 1D.

Fixing the world one invalidator at a time.

Workshop materials for training in scientific computing and scientific machine learning

Finite State Projection algorithms for chemical reaction networks

Differential equation solvers with global error estimation

Helicopter Scientific Machine Learning (SciML) Challenge Problem

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.

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

A deprecated way of handling discrete data in continuous equations

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

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

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

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Simpler stacktraces for the Julia Programming Language

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

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

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

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