Hybrid knowledge-based/deep learning reduced order modelling of high dimensional chaotic systems

Chaotic systems are widespread and can be found everywhere, from small scale processes inside the human body to the large scale dynamics of the entire atmosphere. However, modelling these high dimensional chaotic systems is a difficult task due to the intrinsic nonlinear nature of chaos as well as the accompanied computational cost. Therefore,...

Chaotic systems learning with hybrid echo state network/proper orthogonal decomposition based model

We explore the possibility of combining a knowledge-based reduced order model (ROM) with a reservoir computing approach to learn and predict the dynamics of chaotic systems. The ROM is based on proper orthogonal decomposition (POD) with Galerkin projection to capture the essential dynamics of the chaotic system while the reservoir computing...

Auto-Encoded Reservoir Computing for Turbulence Learning

We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn the dynamics of a 2D turbulent flow. The AE-RC consists of an Autoencoder, which discovers an efficient manifold representation of the flow state, and an Echo State Network, which learns the time evolution of the flow in the manifold. The AE-RC is able to both learn the...