Search results for julia neural ode

24 search results found

Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Scimltutorials.jl ⭐ 698

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Scimlsensitivity.jl ⭐ 291

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.

Diffeqbase.jl ⭐ 282

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

Diffeqoperators.jl ⭐ 279

Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Scimlbenchmarks.jl ⭐ 279

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Diffeqgpu.jl ⭐ 261

GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

Componentarrays.jl ⭐ 258

Arrays with arbitrarily nested named components.

Diffeqdocs.jl ⭐ 251

Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem

Universal_differential_equations ⭐ 187

Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML

Jumpprocesses.jl ⭐ 127

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)

Diffeqbayes.jl ⭐ 119

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

Ode.jl ⭐ 106

Assorted basic Ordinary Differential Equation solvers for scientific machine learning (SciML). Deprecated: Use DifferentialEquations.jl instead.

Diffeqcallbacks.jl ⭐ 77

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

Multiscalearrays.jl ⭐ 69

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

Diffeqparamestim.jl ⭐ 60

Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions

Crnn ⭐ 57

Chemical Reaction Neural Network

Diffeqproblemlibrary.jl ⭐ 51

A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools

Fmiflux.jl ⭐ 49

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to place FMUs (fmi-standard.org) everywhere inside of your ML topologies and still keep the resulting model trainable with a standard (or custom) FluxML training process.

Sciml.ai ⭐ 48

The SciML Scientific Machine Learning Software Organization Website

Boundaryvaluediffeq.jl ⭐ 39

Boundary value problem (BVP) solvers for scientific machine learning (SciML)

Latentdiffeq.jl ⭐ 33

Latent Differential Equations models in Julia.

Regneuralde.jl ⭐ 25

Official Implementation of "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics" (ICML 2021)

Scimltutorialsoutput ⭐ 18

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Control_neuralode ⭐ 9

On the use of NeuralODEs as optimal control policies: I.O. Sandoval, P. Petsagkourakis, E. A. del Rio-Chanona, “Neural ODEs as Feedback Policies for Nonlinear Optimal Control” The 22nd World Congress of the International Federation of Automatic Control (IFAC 2023)

Bridgediffeq.jl ⭐ 5

A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)