3 papers
nlin.CD2026
Adjoint-based optimization with quantized local reduced-order models for spatiotemporally chaotic systems
Defne E. Ozan, Antonio Colanera, Luca Magri
We introduce a computationally efficient and accurate reduced order modelling approach for the optimization of spatiotemporally chaotic systems. The proposed method combines quanti…
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…