Benchmarking Surrogates for coupled ODE systems.

NeuralODE implementation of DER elastic forces.

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.

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.

Warwick Project

Introcution to neural ordinary diferential equations

Training stiff NODE in data-driven wastewater process modelling

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

Attentive Co-Evolving Neural Ordinary Differential Equations

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

Differentiable Reacting Flow Modeling Software

Approximately Bayesian Ensembling for Parameterized Neural ODEs.

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

MeshODE: A Robust and Scalable Framework for Mesh Deformation