neuralode

DENG-MIT/Arrhenius.jl

Differentiable Reacting Flow Modeling Software

ThummeTo/FMIFlux.jl

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.

sheoyon-jhin/ACE-NODE

Attentive Co-Evolving Neural Ordinary Differential Equations

IlyaOrson/Control-NeuralODE

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

vothuckhanhhuyen/neural-ode-pde-sde

dgjung0220/CLODE

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

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

Introduction to neural ordinary diferential equations

efmanu/SubspaceInference.jl

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

robin-janssen/CODES-Benchmark

Benchmarking Surrogates for coupled ODE systems.

farenga/abe-pnode

Approximately Bayesian Ensembling for Parameterized Neural ODEs.

Xiangjun-Huang/training_stiff_NODE_in_WW_modelling

Training stiff NODE in data-driven wastewater process modelling

lilpuzeen/NeuralODE

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

Mirko1996G/flow_induced_compositional_tensor_trains

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.

ryanchaiyakul/dismech_nn

NeuralODE implementation of DER elastic forces.

Thahit/Recommender_Sim

Warwick Project