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

Siena, Pierfrancesco; Africa, Pasquale Claudio; Girfoglio, Michele; Rozza, Gianluigi

doi:10.1016/bs.aams.2024.08.006

Advances in Applied Mechanics

2024

DOI: 10.1016/bs.aams.2024.08.006

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

Pierfrancesco Siena,

Pasquale Claudio Africa,

Michele Girfoglio

et al.

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A brief review of reduced order models using intrusive and non‐intrusive techniques

Padula,

Girfoglio,

Rozza

2024

Proc Appl Math and Mech

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Reduced Order Models (ROMs) have gained a great attention by the scientific community in the last years thanks to their capabilities of significantly reducing the computational cost of the numerical simulations, which is a crucial objective in applications like real time control and shape optimization. This contribution aims to provide a brief overview about such a topic. We discuss both a classic intrusive framework based on a Galerkin projection technique and hybrid/non‐intrusive approaches, including Physics Informed Neural Networks (PINN), purely Data‐Driven Neural Networks (NN), Radial Basis Functions (RBF), Dynamic Mode Decomposition (DMD) and Gaussian Process Regression (GPR). We also briefly mention geometrical parametrization and dimensionality reduction methods like Active Subspaces (ASs). Then we test the performance of such approaches in terms of efficiency and accuracy against three academic test cases, the lid driven cavity, the flow past a cylinder and the geometrically parametrized Stanford Bunny. Moreover, we also briefly present some preliminary results related to a more complex case involving an industrial application.

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A brief review of reduced order models using intrusive and non‐intrusive techniques

Padula,

Girfoglio,

Rozza

2024

Proc Appl Math and Mech

View full text Add to dashboard Cite

show abstract

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

Cited by 1 publication

References 76 publications

A brief review of reduced order models using intrusive and non‐intrusive techniques

A brief review of reduced order models using intrusive and non‐intrusive techniques

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