Physics-Informed Echo State Networks for Chaotic Systems Forecasting. - Nguyen Anh Khoa Doan; Wolfgang Polifke; Luca Magri (ICCS (4), 2019)

A physics-aware machine to predict extreme events in turbulence. - Nguyen Anh Khoa Doan; Wolfgang Polifke; Luca Magri (CoRR, 2019)

Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach. - Nguyen Anh Khoa Doan; Wolfgang Polifke; Luca Magri (ICCS (6), 2020)

Physics-informed echo state networks. - Nguyen Anh Khoa Doan; Wolfgang Polifke; Luca Magri (J. Comput. Sci., 2020)

Auto-Encoded Reservoir Computing for Turbulence Learning. - Nguyen Anh Khoa Doan; Wolfgang Polifke; Luca Magri (ICCS (5), 2021)

Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach. - Nguyen Anh Khoa Doan; Wolfgang Polifke; Luca Magri (CoRR, 2021)

On interpretability and proper latent decomposition of autoencoders. - Luca Magri; Nguyen Anh Khoa Doan (CoRR, 2022)

Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network. - Alberto Racca; Nguyen Anh Khoa Doan; Luca Magri (CoRR, 2022)

Convolutional Autoencoder for the Spatiotemporal Latent Representation of Turbulence. - Nguyen Anh Khoa Doan; Alberto Racca; Luca Magri (ICCS (4), 2023)

Clustering-Based Identification of Precursors of Extreme Events in Chaotic Systems. - Urszula Golyska; Nguyen Anh Khoa Doan (ICCS (4), 2023)

Cluster-Based Reduced-Order Modelling and Control for Chaotic Systems with Extreme Events. - Yuxuan Yang; Nguyen Anh Khoa Doan (ICCS (Workshops 4), 2025)