Hierarchical Model Reduction Techniques for Flow Modeling in a Parametrized Setting
Abstract
In this work we focus on two different methods to deal with parametrized partial differential equations in an efficient and accurate way. Starting from high fidelity approximations built via the hierarchical model reduction discretization, we consider two approaches, both based on a projection model reduction technique. The two methods differ for the algorithm employed during the construction of the reduced basis. In particular, the former employs the proper orthogonal decomposition, while the latter relies on a greedy algorithm according to the certified reduced basis technique. The two approaches are preliminarily compared on two-dimensional scalar and vector test cases.
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© 2021, Society for Industrial and Applied Mathematics.
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Submitted: 4 September 2019
Accepted: 3 December 2020
Published online: 8 February 2021
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Horizon 2020 Framework Programme https://doi.org/10.13039/100010661 : 681447, 872442
Istituto Nazionale di Alta Matematica "Francesco Severi" https://doi.org/10.13039/100009112
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