3 papers
eess.SY2025
Data-assimilated model-informed reinforcement learning
Defne E. Ozan, Andrea Nóvoa, Georgios Rigas +1
The control of spatio-temporally chaos is challenging because of high dimensionality and unpredictability. Model-free reinforcement learning (RL) discovers optimal control policies…
eess.SY2025
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows
Defne E. Ozan, Andrea Nóvoa, Luca Magri
The goal of many applications in energy and transport sectors is to control turbulent flows. However, because of chaotic dynamics and high dimensionality, the control of turbulent…
cs.LG2025
Online model learning with data-assimilated reservoir computers
Andrea Nóvoa, Luca Magri
We propose an online learning framework for forecasting nonlinear spatio-temporal signals (fields). The method integrates (i) dimensionality reduction, here, a simple proper orthog…