Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equation...
#计算机科学#Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
#计算机科学#Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
#计算机科学#A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
#计算机科学#Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
#计算机科学#Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
#计算机科学#Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
#计算机科学#Code for the paper "Learning Differential Equations that are Easy to Solve"
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)
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
#计算机科学#This repository contains code released by DiffEqML Research
#计算机科学#Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
#编辑器#Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools