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Exploration-oriented sampling strategies for global surrogate modeling: A comparison between one-stage and adaptive methods

Lualdi, Pietro and Sturm, Ralf and Siefkes, Tjark (2022) Exploration-oriented sampling strategies for global surrogate modeling: A comparison between one-stage and adaptive methods. Journal of Computational Science. Elsevier. doi: 10.1016/j.jocs.2022.101603. ISSN 1877-7503.

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Official URL: https://www.sciencedirect.com/science/article/abs/pii/S1877750322000357#!


Studying complex phenomena in detail by performing real experiments is often an unfeasible task. Virtual experiments using simulations are usually used to support the development process. However, numerical simulations are limited by their computational cost. Metamodeling techniques are commonly used to mimic the behavior of unknown solver functions, especially for expensive black box optimizations. If a good correlation between the surrogate model and the black box function is obtained, expensive numerical simulations can be significantly reduced. The sampling strategy, which selects a subset of samples that can adequately predict the behavior of expensive black box functions, plays an important role in the fidelity of the surrogate model. Achieving the desired metamodel accuracy with as few solver calls as possible is the main goal of global surrogate modeling. In this paper, exploration-oriented adaptive sampling strategies are compared with commonly used one-stage sampling approaches, such as Latin Hypercube Design (LHD). The difference in the quality of approximation is tested on benchmark functions from 2 up to 30 variables. Two novel sampling algorithms to get fine-grained quasi-LHDs will be proposed and an improvement to a well-known, pre-existing sequential input algorithm will be discussed. Finally, these methods are applied to a crash box design to investigate the performance when approximating highly non-linear crashworthiness problems. It is found that adaptive sampling approaches outperform one-stage methods both in terms of mathematical properties and in terms of metamodel accuracy in the majority of the tests. A proper stopping algorithm should also be employed with adaptive methods to avoid oversampling.

Item URL in elib:https://elib.dlr.de/187723/
Document Type:Article
Title:Exploration-oriented sampling strategies for global surrogate modeling: A comparison between one-stage and adaptive methods
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Lualdi, PietroUNSPECIFIEDhttps://orcid.org/0000-0001-9722-6185UNSPECIFIED
Date:19 February 2022
Journal or Publication Title:Journal of Computational Science
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:Exploration sampling strategies, Adaptive sampling methods, Global surrogate modelling, Crashworthiness optimization
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - FFAE - Fahrzeugkonzepte, Fahrzeugstruktur, Antriebsstrang und Energiemanagement
Location: Stuttgart
Institutes and Institutions:Institute of Vehicle Concepts > Vehicle Architectures and Lightweight Design Concepts
Deposited By: Lualdi, Pietro
Deposited On:04 Aug 2022 13:38
Last Modified:29 Mar 2023 00:02

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