Implementation of (2018) Neural Ordinary Differential Equations on Keras
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Updated
Apr 9, 2019 - Python
Implementation of (2018) Neural Ordinary Differential Equations on Keras
Experimenting with Neural Ordinary Differential Equations (NODE)
Reimplementations of some normalizing flow algorithms using tensorflow 2.1 and tensorflow probability 0.9
neural ordinary differential equations for undergraduate students
Models and code for the ICLR 2020 workshop paper "Towards Understanding Normalization in Neural ODEs"
Putting [A]NODEs ([Augmented] Neural ODEs) to work, for Time Series prediction and simulation
Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support
A component of the SciML scientific machine learning ecosystem for optimal control
Code for "Time-Reversal Symmetric ODE Network (NeurIPS 2020)"
A collection of resources regarding the interplay between control, causality, latent structure and self-supervision.
Repository for the Control of Stochastic Quantum Dynamics with Differentiable Programming paper.
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561
Neural Graph Differential Equations (Neural GDEs)
A short course on simulation-based infernce for physics at YSDA in April 2021
This repository contains the code for training and visualizing the fully-differentiable Kuramoto model, "KuraNet".
Regularized Neural ODEs (RNODE)
Neural Ordinary Differential Equations for model order reduction of time-dependent PDEs
Code and figures for "Neural network differential equations for ion channel modelling".
I created a Graph Neural Ordinary Differential Equation (GNODE) for modeling Newtonian gravity.
CVPR2021 paper "Learning Parallel Dense Correspondence from Spatio-Temporal Descriptorsfor Efficient and Robust 4D Reconstruction"
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