12 citations · 32 across the 14 of their papers we have counts for
3 papers · 1 filter
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…