activity
20182026
most citedHull shape design optimization with parameter space and model reductions, and self-learning mesh morphing

48 citations · 67 across the 35 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

math.NA2026

Model Order Reduction of Cerebrovascular Hemodynamics Using POD_Galerkin and Reservoir Computing_based Approach

Rahul Halder, Arash Hajisharifi, Kabir Bakhshaei +1

We investigate model order reduction (MOR) strategies for simulating unsteady hemodynamics within cerebrovascular systems, contrasting a physics-based intrusive approach with a dat…

math.NA2026

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…

physics.flu-dyn2026

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…

math.NA2026

An efficient hyper reduced-order model for segregated solvers for geometrical parametrization problems

Valentin Nkana Ngan, Giovanni Stabile, Andrea Mola +1

We propose an efficient hyper-reduced order model (HROM) designed for segregated finite-volume solvers in geometrically parametrized problems. The method follows a discretize-then-…

physics.flu-dyn2026

Data-driven reduced order model for residence time distribution analysis of an industrial-scale continuous casting tundish

Harshith Gowrachari, Mattia Giuseppe Barra, Giovanni Stabile +2

The continuous casting tundish plays a critical role as a metallurgical reactor in the continuous casting process, with its flow characteristics serving as a key parameter in the p…