48 citations · 75 across the 4 of their papers we have counts for
6 papers
Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-constrained machine learning method-based on reservoir computing- to time-accurately predict extreme events and long-term velocity statistics in a model of tur…
Auto-Encoded Reservoir Computing for Turbulence Learning
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
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 man…
Physics-Informed Echo State Networks
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve su…
Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the evolution of an unmeasured state (hidden state) in a chaotic system. The PI-ESN is trained b…
A physics-aware machine to predict extreme events in turbulence
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-aware machine learning method to time-accurately predict extreme events in a turbulent flow. The method combines two radically different approaches: empirical…
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
Nguyen Anh Khoa Doan, Wolfgang Polifke, Luca Magri
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve su…