Hierarchical model reduction techniques for flow modeling in a parametrized setting

Matteo Zancanaro, Francesco Ballarin, Simona Perotto, Gianluigi Rozza

Research output: Contribution to journalArticle


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.
Original languageEnglish
Pages (from-to)267-293
Number of pages27
Publication statusPublished - 2021


  • Hierarchical model reduction
  • Parametrized problems
  • Projection-based reduced order modeling
  • Proper orthogonal decomposition
  • Reduced basis method


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