neuralode

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.

julia fmi hybrid-modeling fmu scientific-machine-learning neuralode neuralfmu physicsai

UpdatedApr 25, 2025
Julia

sheoyon-jhin /ACE-NODE

Star29

Attentive Co-Evolving Neural Ordinary Differential Equations

time-series image-classification neuralode kdd2021

UpdatedOct 16, 2023
Python

IlyaOrson /Control-NeuralODE

Star16

Neural ODEs as Feedback Policies for Nonlinear Optimal Control (IFAC 2023)https://doi.org/10.1016/j.ifacol.2023.10.1248

optimization julia automatic-differentiation neural-networks ordinary-differential-equations optimal-control adjoint-sensitivities neuralode

UpdatedAug 10, 2023
Julia

vothuckhanhhuyen /neural-ode-pde-sde

Star8

machine-learning papers neuralode neuralpde neuralsde

UpdatedMar 3, 2022

dgjung0220 /CLODE

Star8

Continuous Exposure Learning for Low-light Image Enhancement using Neural ODEs, in ICLR 2025 (Spotlight)

neuralode llie

UpdatedMar 29, 2025
Python

OscarBergPRIV /Gentle-Introduction-to-neural-ordinary-differential-equations

Star6

Introduction to neural ordinary diferential equations

deeplearning neural-differential-equations neuralode

UpdatedJun 26, 2024
Jupyter Notebook

robin-janssen /CODES-Benchmark

Star5

Benchmarking Surrogates for coupled ODE systems.

ode chemical-reaction-networks fully-connected-network surrogate-models neuralode operator-learning deeponet

UpdatedApr 29, 2025
Python

efmanu /SubspaceInference.jl

Star5

Subspace Inference package for uncertainty analysis in deep neural networks and neural ordinary differential equations using Julia

deep-neural-networks bayesian-inference neuralode subspace-inferences

UpdatedJul 3, 2021
Julia

Xiangjun-Huang /training_stiff_NODE_in_WW_modelling

Star2

Training stiff NODE in data-driven wastewater process modelling

data-driven modelling odes stiff wastewater-treatment neuralode

UpdatedApr 13, 2024
MATLAB

farenga /abe-pnode

Star2

Approximately Bayesian Ensembling for Parameterized Neural ODEs.

ode dynamical-systems ordinary-differential-equations ode-solver neural-ode neuralode

UpdatedNov 17, 2022
Jupyter Notebook

lilpuzeen /NeuralODE

Star1

Finding parameters (values of coefficients) for a system of differential equations with constant coefficients at known values at a number of points.

differential-equations neuralode

UpdatedJan 19, 2024
Python

Thahit /Recommender_Sim

Star0

Warwick Project

python pytorch recommender-system bayesian-deep-learning neuralode generative-ai

UpdatedAug 28, 2024
Jupyter Notebook

Mirko1996G /flow_induced_compositional_tensor_trains

Star0

The core idea is to parametrize the right-hand side of an ordinary differential equation (ODE) using a tensor train (TT) decomposition, such that the discretization of the ODE via standard numerical methods, such as the Explicit Euler scheme, implicitly induces a compositional TT structure.

tensortrain neuralode odeflows