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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
| Date | Stars |
|---|---|
| 2026-07-31 | 1540 |
| 2026-08-05 | 1540 |
| 2026-08-06 | 1540 |
Today
— stars today
This week
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This month
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Momentum
0.0
growth rate 0.00%/day
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<h1>Awesome Neural ODE</h1>
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A collection of resources regarding the interplay between differential equations, dynamical systems, deep learning, control, numerical methods and scientific machine learning.
**NOTE:** Feel free to suggest additions via `Issues` or `Pull Requests`.
The repo further introduces a (rough) categorization by assigning topic labels to each work. These are not supposed to be comprehensive or precise, and should only provide a rough idea of the contents.
# Table of Contents
* **Differential Equations in Deep Learning**
* [General Architectures](#general-architectures)
* [Neural Operators](#neural-operators)
* [Neural ODEs](#neural-odes)
* [Training of Neural ODEs](#training-of-neural-odes)
* [Speeding up continuous models](#speeding-up-continuous-models)
* [Control with Neural ODEs](#control-with-neural-odes)
* [Neural GDEs](#neural-gdes)
* [Neural SDEs](#neural-sdes)
* [Neural CDEs](#neural-cdes)
* [Generative Models](#generative-models)
* [Normalizing Flows](#normalizing-flows)
* [Score-Matching SDEs](#score-matching-sdes)
* [Applications](#applications)
* **Deep Learning Methods for Differential Equations (Scientific ML)**
* [Solving Differential Equations](#solving-differential-equations)
* [Model Discovery](#model-discovery)
* **Dynamical System View of Deep Learning**
* [Recurrent Neural Networks](#recurrent-neural-networks)
* [Theory and Perspectives](#theory-and-perspectives)
* [Optimization](#optimization)
* [Software and Libraries](#software-and-libraries)
* [Websites and Blogs](#websites-and-blogs)
## Differential Equations in Deep Learning
### General Architectures
* Recurrent Neural Networks for Multivariate Time Series with Missing Values: [Scientific Reports18](https://arxiv.org/abs/1606.01865)
> Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. We propose a GRU-based model called GRU-D, in which a decay mechanism is designed for the input variables and the hidden states to capture the aforementioned properties. We introduce decay rates in the model to control the decay mechanism by considering the following important factors.
* Learning unknown ODE models with Gaussian processes: [arXiv18](https://arxiv.org/abs/1803.04303), [code](https://github.com/cagatayyildiz/npde/)
> However, for many complex systems it is practically impossible to determine the equations or
interactions governing the underlying dynamics. In these settings, parametric ODE model cannot be formulated. Here, we overcome this issue by introducing a novel paradigm of nonparametric ODE modeling that can learn the underlying dynamics of arbitrary continuous-time systems without prior knowledge. We propose to learn non-linear, unknown differential functions from state observations using Gaussian process vector fields within the exact ODE formalism.
* Deep Equilibrium Models: [NeurIPS19](https://arxiv.org/abs/1909.01377)
 
> We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers of many existing deep sequenceExcerpt of 32,102 characters
Read on GitHub
Michael Poli · Radical Numerics
110
Zhmin Zhao · Software Analysis and Intelligence Lab (SAIL) & Lab on Maintenance, Construction and Intelligence of Software (MCIS) · Canada
2
Ayan Das · Huawei R&D UK · United Kingdom
1
Christopher Rackauckas · Massachusetts Institute of Technology · United States
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:54cfdc74208e69fb, topic:deep-learning