SciML Open Source Scientific Machine Learning
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- Reservoir computing utilities for scientific machine learning (SciML)
- 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
- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
- A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages
- 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
- Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
- Symbolic-Numeric Neural DAEs and Universal Differential Equations for Automating Scientific Machine Learning (SciML)
- Global documentation for the Julia SciML Scientific Machine Learning Organization
- The SciML Scientific Machine Learning Software Organization Website
ColPrac
Public- 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.
- A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality
ProcessSimulator.jl
PublicDiffEqDevDocs.jl
PublicDeveloper documentation for the SciML scientific machine learning ecosystem's differential equation solversStatic.jl
PublicStochasticDiffEq.jl
PublicSolvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem- SciMLOperators.jl: Matrix-Free Operators for the SciML Scientific Machine Learning Common Interface in Julia
DiffEqFinancial.jl
PublicDifferential equation problem specifications and scientific machine learning for common financial modelsDelayDiffEq.jl
PublicDelay differential equation (DDE) solvers in Julia for the SciML scientific machine learning ecosystem. Covers neutral and retarded delay differential equations, and differential-algebraic equations.- 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.
ModelingToolkitCourse
PublicA course on composable system modeling, differential-algebraic equations, acausal modeling, compilers for simulation, and building digital twins of real-world devicesDASKR.jl
PublicInterface to DASKR, a differential algebraic system solver for the SciML scientific machine learning ecosystem- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.
- High dimensional numbers and reductions recipes for data visualization of scientific machine learning (SciML)