hjwdzh/MeshODE

MeshODE: A Robust and Scalable Framework for Mesh Deformation

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DENG-MIT/Arrhenius.jl

Differentiable Reacting Flow Modeling Software

Language: Jupyter Notebook - Size: 3.11 MB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 59 - Forks: 20

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.

Language: Julia - Size: 185 MB - Last synced at: about 1 month ago - Pushed at: about 2 months ago - Stars: 57 - Forks: 15

IlyaOrson/Control-NeuralODE

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

Language: Julia - Size: 903 KB - Last synced at: 3 months ago - Pushed at: almost 2 years ago - Stars: 16 - Forks: 1

sheoyon-jhin/ACE-NODE

Attentive Co-Evolving Neural Ordinary Differential Equations

Language: Python - Size: 89.8 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 14 - Forks: 1

dgjung0220/CLODE

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

Language: Python - Size: 18.3 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 6 - Forks: 2

robin-janssen/CODES-Benchmark

Benchmarking Surrogates for coupled ODE systems.

Language: Python - Size: 205 MB - Last synced at: 11 days ago - Pushed at: 11 days ago - Stars: 5 - Forks: 2

efmanu/SubspaceInference.jl

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

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farenga/abe-pnode

Approximately Bayesian Ensembling for Parameterized Neural ODEs.

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Xiangjun-Huang/training_stiff_NODE_in_WW_modelling

Training stiff NODE in data-driven wastewater process modelling

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lilpuzeen/NeuralODE

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

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vothuckhanhhuyen/neural-ode-pde-sde

Size: 11.7 KB - Last synced at: over 2 years ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

deepbiolab/reinforce-bio

Research on leveraging reinforcement learning to optimize bioprocess parameters and improve efficiency in biological systems.

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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.

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ryanchaiyakul/dismech_nn

NeuralODE implementation of DER elastic forces.

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Thahit/Recommender_Sim

Warwick Project

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