Vol. 5 No. 1 (2025): Issue 5
Articles

Efficient Finite Element Model Calibration through Polynomial Chaos Expansion

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  • Ahmet Yılmaz,
  • Selin Demir,
  • Osman Kaya
Ahmet Yılmaz
Faculty of Engineering and Natural Sciences, Bursa Technical University, Bursa, Turkey
Selin Demir
Department of Mechanical Engineering, Gaziantep University, Gaziantep, Turkey
Osman Kaya
Computational Mechanics Research Center, Çukurova University, Adana, Turkey
DOI: https://doi.org/10.71070/oaml.v5i1.121

Published 2025-04-15

Keywords

  • Finite Element Model,
  • Polynomial Chaos Expansion,
  • Calibration Process,
  • Optimization Techniques,
  • Computational Efficiency

How to Cite

Yılmaz, A., Demir, S., & Kaya, O. (2025). Efficient Finite Element Model Calibration through Polynomial Chaos Expansion. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.121
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

This study addresses the importance of efficient Finite Element Model (FEM) calibration through Polynomial Chaos Expansion (PCE). Despite the acknowledged significance of FEM in engineering applications, the precise calibration of these models remains challenging due to the computational burden associated with traditional methods. The current research landscape reflects a growing interest in leveraging PCE to streamline and enhance the calibration process. However, existing studies still face limitations in terms of scalability and accuracy. To address these challenges, this paper presents a novel approach that combines PCE with advanced optimization techniques to efficiently calibrate FEMs with improved accuracy and computational efficiency. The innovative methodology proposed in this work aims to overcome the existing limitations, offering a significant advancement in the field of FEM calibration.

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