SciML Open Source Scientific Machine Learning
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- 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)
- Arrays with arbitrarily nested named components.
- 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
- Julia Catalyst.jl importers for various reaction network file formats like BioNetGen and stoichiometry matrices
- The Base interface of the SciML ecosystem
- Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
- 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
- Global documentation for the Julia SciML Scientific Machine Learning Organization
- Fast and automatic structural identifiability software for ODE systems
- Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
- Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
- 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.
- High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.
- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
- A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
- Fast uncertainty quantification for scientific machine learning (SciML) and differential equations
- Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
- Surrogate modeling and optimization for scientific machine learning (SciML)
- 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
- Reservoir computing utilities for scientific machine learning (SciML)
- Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.