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math.NA2026
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…
math.NA2026
A Multi-Fidelity Parametric Framework for Reduced-Order Modeling using Optimal Transport-based Interpolation: Applications to Diffused-Interface Two-Phase Flows
Moaad Khamlich, Niccolò Tonicello, Federico Pichi +1
This work introduces a data-driven, non-intrusive reduced-order modeling (ROM) framework that leverages Optimal Transport (OT) for multi-fidelity and parametric problems in two-pha…