Nguyen Anh Khoa Doan

Proceedings of the Computational Science - ICCS 2025 Workshops, 2025

2023

Clustering-Based Identification of Precursors of Extreme Events in Chaotic Systems.

[BibT_eX]

[DOI]

Urszula Golyska

Proceedings of the Computational Science - ICCS 2023, 2023

Convolutional Autoencoder for the Spatiotemporal Latent Representation of Turbulence.

[BibT_eX]

[DOI]

Alberto Racca

Proceedings of the Computational Science - ICCS 2023, 2023

2022

Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network.

[BibT_eX]

[DOI]

Alberto Racca

CoRR, 2022

On interpretability and proper latent decomposition of autoencoders.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach.

[BibT_eX]

[DOI]

CoRR, 2021

Auto-Encoded Reservoir Computing for Turbulence Learning.

[BibT_eX]

[DOI]

Proceedings of the Computational Science - ICCS 2021, 2021

2020

Physics-informed echo state networks.

[BibT_eX]

[DOI]

J. Comput. Sci., 2020

Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach.

[BibT_eX]

[DOI]

Proceedings of the Computational Science - ICCS 2020, 2020

2019

A physics-aware machine to predict extreme events in turbulence.

[BibT_eX]

[DOI]

CoRR, 2019

Physics-Informed Echo State Networks for Chaotic Systems Forecasting.

[BibT_eX]

[DOI]