Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
-
Updated
Jun 5, 2024 - MATLAB
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
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.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
The SciML Scientific Machine Learning Software Organization Website
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)
Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022
Official PyTorch implementation for the paper Minimizing Trajectory Curvature of ODE-based Generative Models, ICML 2023
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
Arrays with arbitrarily nested named components.
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
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.
Add a description, image, and links to the neural-ode topic page so that developers can more easily learn about it.
To associate your repository with the neural-ode topic, visit your repo's landing page and select "manage topics."