Parallel-in-time integration of Neural ODEs with reduced basis approximation
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Updated
Apr 23, 2024 - Python
Parallel-in-time integration of Neural ODEs with reduced basis approximation
Climate Modeling with Neural Diffusion Equation, ICDM'21
Neural integration for constitutive equations
A brief study conducted as a part of major "Applied mathematics and computer science" at the HSE University.
Experimenting with Neural Ordinary Differential Equations (NODE)
A collection of resources regarding the interplay between control, causality, latent structure and self-supervision.
SIGIR'2024: ReCODE: Modeling Repeat Consumption with Neural ODE
This is a repo for Neural ODE and CDE forecasters.
Neural Ordinary Differential Equations for Inverse Kinematics
Reimplementations of some normalizing flow algorithms using tensorflow 2.1 and tensorflow probability 0.9
Kinetics-Constrained Neural Ordinary Differential Equations (KCNODE) that can be trained even with small data
Code accompanying paper "Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits" at L4DC 2022.
Fit time-series data with a Neural Differential Equation!
Approximately Bayesian Ensembling for Parameterized Neural ODEs.
Graph neural ordinary differential equations
Putting [A]NODEs ([Augmented] Neural ODEs) to work, for Time Series prediction and simulation
[PACIS 2024] The official repo for the paper: "Phase Space Reconstructed Neural Ordinary Differential Equations Model for Stock Price Forecasting".
Repository for notes, projects and snippets on NODEs. Includes results after training CNN based networks with different methods on MNIST, CIFAR-10, CelebA and CatsAndDogs datasets accordingly.
neural ordinary differential equations for undergraduate students
I created a Graph Neural Ordinary Differential Equation (GNODE) for modeling Newtonian gravity.
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