On the accuracy and efficiency of reduced order models: towards real-world applications

TL;DR

This paper reviews various Reduced Order Models (ROMs), comparing their accuracy and efficiency through multiple test cases, and introduces new platforms for real-time data-driven predictions in biomedical and industrial applications.

Contribution

It provides an extensive overview of ROM frameworks, validates them on diverse test cases, and introduces two innovative platforms for real-time applications.

Findings

Data-driven ROMs offer a favorable accuracy-efficiency trade-off.

Validation on real-world problems demonstrates practical applicability.

New platforms ARGOS and ATLAS enable accessible real-time modeling.

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.