Speakers

Title: Structure-preserving reduced order models for conservation laws with source terms Abstract Reduced Order Models (ROMs) have been introduced to overcome the prohibitive computational cost of numerical simulations in multi-query contexts and complex physical phenomena, while maintaining an acceptable accuracy.

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Title: Well-balanced POD-PID-DEIM reduced-order models for nonlinear parametric hyperbolic problems Authors: Irene Gómez-Bueno, Enrique D. Fernández-Nieto, Samuele Rubino Abstract We present a reduced-order modelling strategy for nonlinear parametric hyperbolic systems that combines Proper Orthogonal Decomposition (POD) with Discrete Empirical Interpolation (DEIM) for an efficient approximation of nonlinear terms, and introduces PID (Principal-Interval Decomposition) together with a time-averaging approach.

Title : TBA Abstract TBA

Title: Quantized-local reduced order models (ql-ROMs) for turbulent and extreme flows Authors: Luca Magri, Antonio Colanera Abstract The long-term behavior of dissipative dynamical systems often evolves on low-dimensional manifolds embedded within the high-dimensional phase space.

Title: Reference-free and reference-guided reinforcement learning for evolve-filter regularization of convection-dominated flows Abstract This talk proposes a reinforcement learning (RL) framework for the dynamic selection of the filter parameter in Evolve–Filter (EF) regularization of incompressible turbulent flows.

Title: Collocation-based model order reduction with nonlinear corrections on moving Chimera grids Abstract Projection-based Model Order Reduction (pMOR) techniques are widely used to reduce the computational cost of high-fidelity simulations of parametrized partial differential equations.

Title: Bayesian data assimilation for reduced-order modelling of plasma instabilities in Hall thrusters Abstract Hall effect thrusters are a key technology for space propulsion due to their high efficiency, yet their operation may be affected by low-frequency oscillations of the discharge current, known as “breathing mode”.

Title: Enriching continuous Lagrange finite element approximation spaces using neural networks Abstract In this work, we present a study combining two approaches in the context of solving PDEs: the continuous finite element method (FEM) and more recent techniques based on neural networks.

Title: Marginal-constrained modified Wasserstein barycenters for model order reduction in quantum chemistry Authors: Virginie Ehrlacher, Maxime Daléry, Geneviève Dusson and Alexeï Lozinski Abstract The aim of this work is to present new approaches to define Wasserstein-like barycenters for Gaussian distributions and Gaussian mixtures, while imposing the marginals of the barycenter.