7 papers
Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
Gabriele Codega, Anna Ivagnes, Nicola Demo +1
In the present work, we introduce a data-driven approach to enhance the accuracy of non-intrusive Reduced Order Models (ROMs). In particular, we focus on ROMs built using Proper Or…
Efficient and Accurate Surrogate Modeling of Turbulent Flows via Space-Dependent Aggregation and Reduced Order Models
Piero Zappi, Anna Ivagnes, Niccolò Tonicello +1
Reynolds-Averaged Navier-Stokes (RANS) models are widely used for turbulent flow simulations due to their computational efficiency, but their accuracy strongly depends on the selec…
Reinforcement Learning-Based Filters for Convection-Dominated Flows: Reference-Free and Reference-Guided Training
Anna Ivagnes, Maria Strazzullo, Gianluigi Rozza
We propose a reinforcement learning (RL) framework for the dynamic selection of the filter parameter in Evolve-Filter (EF) regularization strategies for incompressible turbulent fl…
StabOp: A Data-Driven Stabilization Operator for Reduced Order Modeling
Ping-Hsuan Tsai, Anna Ivagnes, Annalisa Quaini +2
Spatial filters have played a central role in large eddy simulation and, more recently, in reduced order model (ROM) stabilization for convection-dominated flows. Nevertheless, imp…
A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation
Anna Ivagnes, Toby van Gastelen, Syver Døving Agdestein +3
We present a novel approach to define the filter and relax steps in the evolve-filter-relax (EFR) framework for simulating turbulent flows. The EFR main advantages are its ease of…
Data-driven Closure Strategies for Parametrized Reduced Order Models via Deep Operator Networks
Anna Ivagnes, Giovanni Stabile, Gianluigi Rozza
In this paper, we propose an equation-based parametric Reduced Order Model (ROM), whose accuracy is improved with data-driven terms added into the reduced equations. These addition…