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Abstract
Chapter 1 was focused on data-driven (or approximation-based) modeling methods. The second major class of surrogates are physics-based models outlined in this chapter. Although they are not as popular, their importance is growing because of the challenges related to construction and handling of approximation surrogates for many real-world problems. The high cost of evaluating computational models, nonlinearity of system responses, dimensionality issues as well as combinations of these factors, may lead to a situation, where setting up a data-driven model is not possible or at least not practical. On the other hand, incorporation of the problem-specific knowledge, typically in the form of a lower-fidelity computational model, often alleviates the aforementioned difficulties. The enhancement of the low-fidelity models using a limited amount of high-fidelity data is the essence of physics-based surrogate modeling. This chapter provides a brief characterization of this class of surrogates, explains the concept and various types of low-fidelity models, as well as outlines several specific modeling approaches, also in the context of surrogate-assisted optimization.
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- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Pietrenko-Dąbrowska A., Kozieł S.: Fundamentals of Physics-Based Surrogate Modeling// Surrogate Modeling for High-Frequency Design. Recent Advances/ : , 2022, s.39-79
- DOI:
- Digital Object Identifier (open in new tab) 10.1142/9781800610750_0002
- Verified by:
- Gdańsk University of Technology
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