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Reduced order models (ROM) can represent spatiotemporal processes in significantly fewer dimensions and can be solved many orders faster than their governing partial differential equations (PDEs). For example, using a proper orthogonal…

Machine Learning · Computer Science 2025-12-24 Shane X. Coffing , John Tipton , Arvind T. Mohan , Darren Engwirda

In this work the development of a machine learning-based Reduced Order Model (ROM) for the investigation of hemodynamics in a patient-specific configuration of Coronary Artery Bypass Graft (CABG) is proposed. The computational domain is…

Medical Physics · Physics 2023-08-08 Pierfrancesco Siena , Michele Girfoglio , Francesco Ballarin , Gianluigi Rozza

We apply reduced-order modeling (ROM) techniques to single-phase flow in faulted porous media, accounting for changing rock properties and fault geometry variations using a radial basis function mesh deformation method. This approach…

Numerical Analysis · Mathematics 2024-03-07 Enrico Ballini , Luca Formaggia , Alessio Fumagalli , Anna Scotti , Paolo Zunino

We developed a novel reduced-order multi-scale method for solving large time-domain wavefield simulation problems. Our algorithm consists of two main stages. During the first "off-line" stage the fine-grid operator (of the graph Laplacian…

Numerical Analysis · Mathematics 2017-03-28 Vladimir Druskin , Alexander V. Mamonov , Mikhail Zaslavsky

This paper proposes a large eddy simulation reduced order model(LES-ROM) framework for the numerical simulation of realistic flows. In this LES-ROM framework, the proper orthogonal decomposition(POD) is used to define the ROM basis and a…

Fluid Dynamics · Physics 2015-10-12 Xuping Xie , David Wells , Zhu Wang , Traian Iliescu

Reduced order modeling (ROM) is a field of techniques that approximates complex physics-based models of real-world processes by inexpensive surrogates that capture important dynamical characteristics with a smaller number of degrees of…

Machine Learning · Computer Science 2021-08-30 Rachel Cooper , Andrey A. Popov , Adrian Sandu

Regardless of the development of various finite element methods for fluid-structure interaction (FSI) problems, optimal-order convergence of finite element discretizations of the FSI problems in the $L^\infty(0,T;L^2)$ norm has not been…

Numerical Analysis · Mathematics 2025-02-25 Erik Burman , Buyang Li , Rong Tang

We propose a projection-based model order reduction procedure for the ageing of large prestressed concrete structures. Our work is motivated by applications in the nuclear industry, particularly in the simulation of containment buildings.…

Reduced-order modeling (ROM) commonly refers to the construction, based on a few solutions (referred to as snapshots) of an expensive discretized partial differential equation (PDE), and the subsequent application of low-dimensional…

Numerical Analysis · Mathematics 2019-05-22 Martin Hess , Alessandro Alla , Annalisa Quaini , Gianluigi Rozza , Max Gunzburger

Nonintrusive projection-based reduced order models (ROMs) are essential for dynamics prediction in multi-query applications where access to the source of the underlying full order model (FOM) is unavailable; that is, FOM is a black-box.…

Computational Physics · Physics 2024-10-16 Aviral Prakash , Yongjie Jessica Zhang

We present an operator-splitting scheme for fluid-structure interaction (FSI) problems in hemodynamics, where the thickness of the structural wall is comparable to the radius of the cylindrical fluid domain. The equations of linear…

Numerical Analysis · Mathematics 2015-10-28 Martina Bukac , Suncica Canic , Roland Glowinski , Boris Muha , Annalisa Quaini

In this paper, we propose two monolithic fully discrete finite element methods for fluid-structure interaction (FSI) based on a novel Piola-type Arbitrary Lagrangian-Eulerian (ALE) mapping. For the temporal discretization, we apply the…

Numerical Analysis · Mathematics 2026-04-09 Shuaijun Liu , Xiaoping Xie

This chapter provides an extended overview about Reduced Order Models (ROMs), with a focus on their features in terms of efficiency and accuracy. In particular, the aim is to browse the more common ROM frameworks, considering both intrusive…

Numerical Analysis · Mathematics 2024-09-04 Pierfrancesco Siena , Paquale Claudio Africa , Michele Girfoglio , Gianluigi Rozza

In this paper we consider the numerical approximation of a semilinear reaction-diffusion model problem (PDEs) by means of reduced order methods (ROMs) based on proper orthogonal decomposition (POD). We focus on the time integration of the…

Numerical Analysis · Mathematics 2026-03-05 Bosco García-Archilla , Alicia García-Mascaraque , Julia Novo

In the context of simulation-based methods, multiple challenges arise, two of which are considered in this work. As a first challenge, problems including time-dependent phenomena with complex domain deformations, potentially even with…

Numerical Analysis · Mathematics 2023-12-06 Fabian Key , Max von Danwitz , Francesco Ballarin , Gianluigi Rozza

Even with the most advanced computational capabilities, high-fidelity (e.g., large-eddy) simulations of large-scale rocket engines remain far out of reach. In the current work, we develop and establish a component-based reduced-order…

Fluid Dynamics · Physics 2026-04-20 Brody Gatza , Cheng Huang

In this study, we present a non-intrusive reduced order modeling (ROM) framework for large-scale quasi-stationary systems. The framework proposed herein exploits the time series prediction capability of long short-term memory (LSTM)…

Computational Engineering, Finance, and Science · Computer Science 2019-11-20 Sk. Mashfiqur Rahman , Suraj Pawar , Omer San , Adil Rasheed , Traian Iliescu

A novel numerical formulation for solving fluid-structure interaction (FSI) problems is proposed where the fluid field is spatially discretized using smoothed particle hydrodynamics (SPH) and the structural field using the finite element…

Computational Engineering, Finance, and Science · Computer Science 2021-06-16 Sebastian L. Fuchs , Christoph Meier , Wolfgang A. Wall , Christian J. Cyron

Reduced order models (ROMs) play a critical role in fluid mechanics by providing low-cost predictions, making them an attractive tool for engineering applications. However, for ROMs to be widely applicable, they must not only generalise…

Machine Learning · Computer Science 2025-05-06 Ismaël Zighed , Nicolas Thome , Patrick Gallinari , Taraneh Sayadi

Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. In this work, we develop data-driven reduced-order models (ROMs) for…

Numerical Analysis · Mathematics 2025-02-05 Ping-Hsuan Tsai , Seung Whan Chung , Debojyoti Ghosh , John Loffeld , Youngsoo Choi , Jonathan L. Belof