Adaptive Model Hierarchies for Multi-Query Scenarios
Kleikamp, Hendrik; Ohlberger, Mario
Research article in edited proceedings (conference) | Peer reviewedAbstract
In this contribution we present an abstract framework for adaptive model hierarchies together with several instances of hierarchies for specific applications. The hierarchy is particularly useful when integrated within an outer loop, for instance an optimization iteration or a Monte Carlo estimation where for a large set of requests answers fulfilling certain criteria are required.
Details about the publication
Editors: Körner, Andreas; Kugi, Andreas; Kemmetmüller, Wolfgang; Deutschmann-Olek, Andreas; Steinböck, Andreas; Hartl-Nesic, Christian; Jadachowski, Lukasz Piotr
Book title: MATHMOD 2025 - Discussion Contribution Volume
Page range: 15-16
Publisher: ARGESIM Verlag
Place of publication: Wien
Status: Published
Release year: 2025
Language in which the publication is written: English
Conference: MATHMOD 2025, Wien, Austria
DOI: 10.34726/9007
Keywords: model hierarchies; adaptivity; model order reduction; machine learning
Authors from the University of Münster
| Kleikamp, Hendrik | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) |
| Ohlberger, Mario | Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger) |
Projects the publication originates from
Duration: 01/01/2019 - 31/12/2025 | 1st Funding period Funded by: DFG - Cluster of Excellence Type of project: Main DFG-project hosted at University of Münster |
Promotionen, aus denen die Publikation resultiert
| Parametrized optimal control and transport-dominated problems: Reduced basis methods, nonlinear reduction strategies and data driven surrogates Candidate: Kleikamp, Hendrik | Supervisors: Ohlberger, Mario | Reviewers: Ohlberger, Mario; Breiten, Tobias Period of time: 01/01/2021 - 19/12/2024 Doctoral examination procedure finished at: Doctoral examination procedure at University of Münster |