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- A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
- Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
- 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
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- A standard library of components to model the world and beyond
- A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
- Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
- High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
- Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
- Symbolic-Numeric Universal Differential Equations for Automating 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.
- 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
- A common solve function for scientific machine learning (SciML) and beyond
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
- Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
SciMLExpectations.jl
PublicFast uncertainty quantification for scientific machine learning (SciML) and differential equations- Boundary value problem (BVP) solvers for scientific machine learning (SciML)
- The Base interface of the SciML ecosystem
- 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.
- SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
- A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
- 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.