7 citations · 11 across the 12 of their papers we have counts for
4 papers · 1 filter
Real-time forecasting of chaotic dynamics from sparse data and autoencoders
Elise Özalp, Andrea Nóvoa, Luca Magri
The real-time prediction of chaotic systems requires a nonlinear-reduced order model (ROM) to forecast the dynamics, and a stream of data from sensors to update the ROM. Data-drive…
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…
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…
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…