88 citations · 876 across the 55 of their papers we have counts for
8 papers · 1 filter
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…
Variational optimization and data assimilation in chaotic time-delayed systems with automatic-differentiated shadowing sensitivity
Nisha Chandramoorthy, Luca Magri, Qiqi Wang
In this computational paper, we perform sensitivity analysis of long-time (or ensemble) averages in the chaotic regime using the shadowing algorithm. We introduce automatic differe…
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…
Degenerate perturbation theory in thermoacoustics: high-order sensitivities and exceptional points
Alessandro Orchini, Luca Magri, Camilo F. Silva +2
In this study, we connect concepts that have been recently developed in thermoacoustics, specifically, (i) high-order spectral perturbation theory, (ii) symmetry induced degenerate…
A data-driven kinematic model of a ducted premixed flame
Hans Yu, Matthew P. Juniper, Luca Magri
Reduced-order models of flame dynamics can be used to predict and mitigate the emergence of thermoacoustic oscillations in the design of gas turbine and rocket engines. This proces…
First-principles machine learning modelling of COVID-19
Luca Magri, Nguyen Anh Khoa Doan
The coronavirus disease 2019 (COVID-19) has changed the world since the World Health Organization declared its outbreak on 30th January 2020, recognizing the outbreak as a pandemic…
Learning ergodic averages in chaotic systems
Francisco Huhn, Luca Magri
We propose a physics-informed machine learning method to predict the time average of a chaotic attractor. The method is based on the hybrid echo state network (hESN). We assume tha…
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…