20 citations · 25 across the 4 of their papers we have counts for
5 papers
Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network
Alberto Racca, Nguyen Anh Khoa Doan, Luca Magri
The spatiotemporal dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of accurate and stable reduced-order models challenging. The overarching o…
Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron
Nilam Tathawadekar, Nguyen Anh Khoa Doan, Camilo F. Silva +1
This paper demonstrates the ability of neural networks to reliably learn the nonlinear flame response of a laminar premixed flame, while carrying out only one unsteady CFD simulati…
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…
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…