Differential Informed Auto-Encoder
This work addresses data generation and structure learning for applications in physics or simulation, but appears incremental as it builds on existing auto-encoder and physics-informed neural network techniques.
The authors tackled the problem of learning the inner structure of data through differential equations, resulting in a method that generates new data adhering to the learned differential structure using a physics-informed neural network.
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.