Recent advances and applications of surrogate models for finite element method computations: a review

The utilization of surrogate models to approximate complex systems has recently gained increased popularity. Because of their capability to deal with black-box problems and lower computational requirements, surrogates were successfully utilized by researchers in various engineering and scientific fields. An efficient use of surrogates can bring considerable savings in computational resources and time. Since literature on surrogate modelling encompasses a large variety of approaches, the appropriate choice of a surrogate remains a challenging task. This review discusses significant publications where surrogate modelling for finite element method-based computations was utilized. We familiarize the reader with the subject, explain the function of surrogate modelling, sampling and model validation procedures, and give a description of the different surrogate types. We then discuss main categories where surrogate models are used: prediction, sensitivity analysis, uncertainty quantification, and surrogate-assisted optimization, and give detailed account of recent advances and applications. We review the most widely used and recently developed software tools that are used to apply the discussed techniques with ease. Based on a literature review of 180 papers related to surrogate modelling, we discuss major research trends, gaps, and practical recommendations. As the utilization of surrogate models grows in popularity, this review can function as a guide that makes surrogate modelling more accessible.

Keywords

Surrogate model, Surrogate-assisted optimization, Sensitivity analysis, Uncertainty quantification, Finite element method

Citation

SOFT COMPUTING. 2022, vol. 26, issue 1, p. 13709-13733.
https://link.springer.com/article/10.1007/s00500-022-07362-8

Document type

Peer-reviewed

Document version

Accepted version

Language of document

en

Document licence

(C) Springer