12 citations · 32 across the 14 of their papers we have counts for
14 papers
A Nonlinear Physics-based Reduced Order Model with Convolutional-based Operator Compression
Anna Ivagnes, Giovanni Stabile, Gianluigi Rozza
Reduced-order models (ROMs) are widely used to accelerate the solution of parametrized partial differential equations, with projection-based methods relying on a linear representat…
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…
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…
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…
Data-driven Optimization for the Evolve-Filter-Relax regularization of convection-dominated flows
Anna Ivagnes, Maria Strazzullo, Michele Girfoglio +2
Numerical stabilization techniques are often employed in under-resolved simulations of convection-dominated flows to improve accuracy and mitigate spurious oscillations. Specifical…
Parametric Intrusive Reduced Order Models enhanced with Machine Learning Correction Terms
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…
Enhancing non-intrusive Reduced Order Models with space-dependent aggregation methods
Anna Ivagnes, Niccolò Tonicello, Paola Cinnella +1
In this manuscript, we combine non-intrusive reduced order models (ROMs) with space-dependent aggregation techniques to build a mixed-ROM. The prediction of the mixed formulation i…
A Shape Optimization Pipeline for Marine Propellers by means of Reduced Order Modeling Techniques
Anna Ivagnes, Nicola Demo, Gianluigi Rozza
In this paper, we propose a shape optimization pipeline for propeller blades, applied to naval applications. The geometrical features of a blade are exploited to parametrize it, al…
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
Anna Ivagnes, Nicola Demo, Gianluigi Rozza
In this work, we propose a model order reduction framework to deal with inverse problems in a non-intrusive setting. Inverse problems, especially in a partial differential equation…
Hybrid Data-Driven Closure Strategies for Reduced Order Modeling
Anna Ivagnes, Giovanni Stabile, Andrea Mola +2
In this paper, we propose hybrid data-driven ROM closures for fluid flows. These new ROM closures combine two fundamentally different strategies: (i) purely data-driven ROM closure…
Pressure Data-Driven Variational Multiscale Reduced Order Models
Anna Ivagnes, Giovanni Stabile, Andrea Mola +2
In this paper, we develop data-driven closure/correction terms to increase the pressure and velocity accuracy of reduced order models (ROMs) for fluid flows. Specifically, we propo…