A Trust Region RB-ML-ROM Approach for Parabolic PDE Constrained Optimization
Klein, Benedikt; Ohlberger, Mario
Research article in edited proceedings (conference) | Peer reviewedAbstract
Optimization problems constrained by parabolic PDEs are common in science and engineering, involving challenges like optimal control and inverse problems. Traditional methods require numerous iterations to solve discretized PDEs, often creating a computational bottleneck. Surrogate models, especially reduced order models (ROM) obtained from reduced basis (RB) methods, offer increased efficiency by approximating these high-fidelity solutions.This contribution explores how machine learning (ML) can enhance surrogate model construction in the framework of error aware trust region (TR) optimization methods.
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: 1-2
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: 10.34726/9000
Link to the full text: https://doi.org/10.34726/9000
Keywords: optimal control; trust region; reduced basis method; machine learning; parabolic PDE
Authors from the University of Münster
| Klein, Benedikt Simon | 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 |