Using DiffEqFlux to learn underlying differential equations from data.
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Updated
Mar 10, 2020 - Jupyter Notebook
Using DiffEqFlux to learn underlying differential equations from data.
A 30-minute showcase on the how and the why of neural differential equations.
Code for "Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations"
Repository for my master thesis at EPFL: "Neural controlled differential equations for crop classification"
Code for the paper "Learning Differential Equations that are Easy to Solve"
This repository contains code released by DiffEqML Research
Awesome-spatial-temporal-data-mining-packages. Julia and Python resources on spatial and temporal data mining. Mathematical epidemiology as an application. Most about package information. Data Sources Links and Epidemic Repos are also included. Keep updating.
Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
Official repository for the paper "Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules" (NeurIPS 2022)
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
Sampling from the solution of the Zakai equation, using the Signature and Conditional Wasserstein GANs
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Codes for paper "Estimating time-varying reproduction number by deep learning techniques"
A Julia package for training recurrent neural networks (RNNs), vanilla neural ordinary differential equations (nODEs) and gated neural ordinary differential equations (gnODEs).
Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
Instantiate neural differential equations with ease
Tutorials on math epidemiology and epidemiology informed deep learning methods
A curated list of awesome Scientific Machine Learning (SciML) papers, resources and software
Code for the TMLR 2023 paper "GRAM-ODE: Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting"
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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