# Differential Informed Auto-Encoder

arXiv: https://arxiv.org/abs/2410.18593 · HTML: https://mlsanity.com/paper/2410.18593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1711.10561/) (2017)

## Abstract (arXiv)

In this article, an encoder was trained to obtain the inner structure of the original data by obtain a differential equations. A decoder was trained to resample the original data domain, to generate new data that obey the differential structure of the original data using the physics-informed neural network.