7 citations · 11 across the 12 of their papers we have counts for
13 papers
Transferable inference of turbulence models for urban flows with the Parameter-Regularised Ensemble Kalman Filter
Emanuele Bombardi, Andrea Nóvoa, Luca Magri +1
The accurate simulation of urban flow is key to designing building ventilation, understanding cities' micrometeorology, and predicting pollutant dispersion. Reynolds-Averaged Navie…
Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry
G. Li Causi, N. Tonicello, L. Magri +1
Autoencoders (AEs) have emerged as powerful tools for non-linear dimensionality reduction, often surpassing traditional linear methods such as Proper Orthogonal Decomposition (POD)…
Precursors of extreme events and critical transitions
Riccardo Consonni, Luca Magri
We propose a theory based on dynamical systems to explain and predict the occurrence of extreme events, of which critical transitions form a subset. In fast-slow nonlinear systems,…
Prediction of chaotic dynamics from data: An introduction
Luca Magri, Andrea Nóvoa, Elise Özalp
This chapter offers a principled approach to the prediction of chaotic systems from data. First, we introduce some concepts from dynamical systems' theory and chaos theory. Second,…
Latent attention on masked patches for flow reconstruction
Ben Eze, Luca Magri, Andrea Nóvoa
Vision transformers have shown outstanding performance in image generation, yet their adoption in fluid dynamics remains limited. We introduce the Latent Attention on Masked Patche…
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…
Efficient Adaptation of ROMs for Unsteady Flows Using Data Assimilation
Ismaël Zighed, Andrea Nóvoa, Luca Magri +1
We propose an efficient retraining strategy for a parameterized Reduced Order Model (ROM) that attains accuracy comparable to full retraining while requiring only a fraction of the…
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…
Adjoint Sensitivities of Chaotic Flows without Adjoint Solvers: A Data-Driven Approach
Defne E. Ozan, Luca Magri
In one calculation, adjoint sensitivity analysis provides the gradient of a quantity of interest with respect to all system's parameters. Conventionally, adjoint solvers need to be…
Data-driven computation of adjoint sensitivities without adjoint solvers: An application to thermoacoustics
Defne E. Ozan, Luca Magri
Adjoint methods have been the pillar of gradient-based optimization for decades. They enable the accurate computation of a gradient (sensitivity) of a quantity of interest with res…