{ "id": "2407.03325", "version": "v1", "published": "2024-04-30T15:05:12.000Z", "updated": "2024-04-30T15:05:12.000Z", "title": "On the accuracy and efficiency of reduced order models: towards real-world applications", "authors": [ "Pierfrancesco Siena", "Paquale Claudio Africa", "Michele Girfoglio", "Gianluigi Rozza" ], "categories": [ "math.NA", "cs.NA" ], "abstract": "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 and data-driven approaches. We present the validation of such techniques against several test cases. The first one is an academic benchmark, the thermal block problem, where a Poisson equation is considered. Here a classic intrusive ROM framework based on a Galerkin projection scheme is employed. The second and third test cases come from real-world applications, the one related to the investigation of the blood flow patterns in a patient specific coronary arteries configuration where the Navier Stokes equations are addressed and the other one concerning the granulation process within pharmaceutical industry where a fluid-particle system is considered. Here we employ two data-driven ROM approaches showing a very relevant trade-off between accuracy and efficiency. In the last part of the contribution, two novel technological platforms, ARGOS and ATLAS, are presented. They are designed to provide a user-friendly access to data-driven models for real-time predictions for complex biomedical and industrial problems.", "revisions": [ { "version": "v1", "updated": "2024-04-30T15:05:12.000Z" } ], "analyses": { "keywords": [ "reduced order models", "real-world applications", "efficiency", "patient specific coronary arteries configuration", "rom framework" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable" } } }