A data driven reduced order model of fluid flow by Auto-Encoder and self-attention deep learning methods

Understand

This paper presents a new data-driven non-intrusive reduced-order model(NIROM) that outperforms the traditional Proper orthogonal decomposition (POD) based reducedorder model.

• This is achieved by using Auto-Encoder(AE) and attention-based deep learning methods.
• The novelty of the present work lies in that it uses Stacked AutoEncoder(SAE) network to project original high-dimensional dynamical systems onto a low dimensional nonlinear subspace and predict the fluid dynamics using an attentionbased deep learning method.
• A new model reduction neural network architecture for fluid flow problem is presented.