Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
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Updated
Sep 29, 2024 - MATLAB
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
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
A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers
The SciML Scientific Machine Learning Software Organization Website
Symbolic-Numeric Universal Differential Equations for Automating Scientific Machine Learning (SciML)
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
A ReCoDE Project Introducing Neural Ordinary Differential Equations starting from ODE theory, working through differentiable implementations of integrators, and finally incorporating neural networks into the solution.
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
Climate Modeling with Neural Diffusion Equation, ICDM'21
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)
Arrays with arbitrarily nested named components.
[PACIS 2024] The official repo for the paper: "Phase Space Reconstructed Neural Ordinary Differential Equations Model for Stock Price Forecasting".
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
LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
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