Machine Learning Techniques and Surrogate Models in Designing, Optimizing, and Analyzing Engineering Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 7
Special Issue Editor
Special Issue Information
Dear Colleagues,
In the design, optimization, and analysis of engineering systems, various physical models, such as the finite element method and computational fluid dynamics, are constructed to evaluate the performance of the proposed design scheme and adjustment strategy. Machine learning techniques and surrogate models, such as support vector regression, Kriging method, artificial neural networks, and polynomial regression, have been used to replace these physical models, aiming to accelerate the design, optimization, and analysis of engineering systems. Applicable areas include mechanical engineering, electrical engineering, civil engineering, and more. Currently, no machine learning technique or surrogate model exhibits competitive performance for all engineering problems. The adaptabilities of different machine learning techniques or surrogate models should be validated, and some improvement work needs to be conducted for special problems and applications.
This Special Issue aims to collect papers that explore and apply machine learning techniques and surrogate models across a range of engineering domains, with particular emphasis on advances addressing specialized engineering problems and applications.
Dr. Maolin Shi
Guest Editor
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Keywords
- machine learning
- surrogate model
- engineering system
- sensitivity analysis
- regression
- approximation
- interpolation
- optimization
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