Obey validity limits of data-driven models through topological data analysis and one-class classification
Publication Date
2021-05-12Journal Title
Optimization and Engineering
ISSN
1389-4420
Publisher
Springer US
Volume
23
Issue
2
Pages
855-876
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Schweidtmann, A. M., Weber, J. M., Wende, C., Netze, L., & Mitsos, A. (2021). Obey validity limits of data-driven models through topological data analysis and one-class classification. Optimization and Engineering, 23 (2), 855-876. https://doi.org/10.1007/s11081-021-09608-0
Description
Funder: RWTH Aachen (3131)
Abstract
Abstract: Data-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in model-based optimization of design and/or operation of processes. Thus, it is critical to ensure that data-driven models are not evaluated outside their validity domain during process optimization. We propose a method to learn this validity domain and encode it as constraints in process optimization. We first perform a topological data analysis using persistent homology identifying potential holes or separated clusters in the training data. In case clusters or holes are identified, we train a one-class classifier, i.e., a one-class support vector machine, on the training data domain and encode it as constraints in the subsequent process optimization. Otherwise, we construct the convex hull of the data and encode it as constraints. We finally perform deterministic global process optimization with the data-driven models subject to their respective validity constraints. To ensure computational tractability, we develop a reduced-space formulation for trained one-class support vector machines and show that our formulation outperforms common full-space formulations by a factor of over 3000, making it a viable tool for engineering applications. The method is ready-to-use and available open-source as part of our MeLOn toolbox (https://git.rwth-aachen.de/avt.svt/public/MeLOn).
Keywords
Research Article, Topological data analysis, Persistent homology, One-class support vector machine, Deterministic global optimization, Machine-learning
Identifiers
s11081-021-09608-0, 9608
External DOI: https://doi.org/10.1007/s11081-021-09608-0
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337024
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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