Speakers

Title: Collocation-based model order reduction: analysis and applications Abstract We consider a novel reduced-order modeling strategy, termed collocation Model Order Reduction (cMOR), designed as an alternative to classical projection-based Model Order Reduction (pMOR).

Title: Some applications of data-driven ROMs at Michelin Abstract Using modern model reduction and machine learning techniques, virtual Michelin tires are now used for in-house design, in driving simulators, and to define tire-oriented services for our customers.

Title: A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation Authors: Anna Ivagnes, Toby van Gastelen, Syver Agdestein, Benjamin Sanderse, Giovanni Stabile and Gianluigi Rozza. Abstract The talk will focus on a novel data-driven surrogate modeling approach for defining the filter and relax steps within the Evolve–Filter–Relax (EFR) framework for turbulent flow simulations.

Title : TBA Abstract TBA

Title: Projection-based reduced order model of linear sloshing with geometric parameters using isogeometric analysis Abstract This study concerns linear sloshing problems of liquids in moving containers [1] and parameterized reduced-order modeling (ROM) [2] using Isogeometric Analysis (IGA) [3].

Title: Conformal variational structure-preserving discretisations: a basis for structure-preserving ROMs. Authors: Damiano Lombardi, Cecilia Pagliantini Abstract In this talk we are going to present a mixed variational formulation for problems described by parametric PDEs.

Title: Calibration-based model order reduction for hyperbolic problems with self-similar travelling discontinuities Abstract Model order reduction (MOR) has been a fundamental tool for reducing computational costs in parametrized (differential) problems. It works on the basis idea that the solutions for different parameters belongs to a reduced linear space with a very small dimension, with respect to classical Finite Element/Finite Volume spaces.

Title: Neural network based method for convection diffusion: eulerian toward lagrangian methods Abstract TBA

Title: Interpretable latent dynamics via graph convolutional networks Abstract Graph Neural Networks (GNNs) have emerged as powerful tools for nonlinear Model Order Reduction (MOR) of time-dependent parameterized Partial Differential Equations (PDEs).

Title: From conservation laws to digital twins: towards structure-preserving ROMs with physics-constrained learning Abstract Conservation laws lie at the heart of CFD and many multiphysics applications. Yet many real-world workflows, such as design optimisation, control, uncertainty-aware decision making and emerging digital-twin pipelines, remain constrained by the cost of resolving multiscale physics at high fidelity.