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01/31/2023
Convolutional autoencoder for the spatiotemporal latent representation of turbulence
Turbulence is characterised by chaotic dynamics and a high-dimensional s...
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11/21/2022
Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network
The spatiotemporal dynamics of turbulent flows is chaotic and difficult ...
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02/15/2021
Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
We propose a physics-constrained machine learning method-based on reserv...
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12/20/2020
Auto-Encoded Reservoir Computing for Turbulence Learning
We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn...
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01/06/2020
Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach
We extend the Physics-Informed Echo State Network (PI-ESN) framework to ...
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12/23/2019
A physics-aware machine to predict extreme events in turbulence
We propose a physics-aware machine learning method to time-accurately pr...
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04/09/2019