@Article{cmes.2024.052203,
AUTHOR = {Shuai Li, Xiaodong Zhao, Jinghu Zhou, Xiyue Wang},
TITLE = {Quantifying Uncertainty in Dielectric Solids’ Mechanical Properties Using Isogeometric Analysis and Conditional Generative Adversarial Networks},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {140},
YEAR = {2024},
NUMBER = {3},
PAGES = {2587--2611},
URL = {http://www.techscience.com/CMES/v140n3/57266},
ISSN = {1526-1506},
ABSTRACT = {Accurate quantification of the uncertainty in the mechanical characteristics of dielectric solids is crucial for advancing their application in high-precision technological domains, necessitating the development of robust computational methods. This paper introduces a Conditional Generation Adversarial Network Isogeometric Analysis (CGAN-IGA) to assess the uncertainty of dielectric solids’ mechanical characteristics. IGA is utilized for the precise computation of electric potentials in dielectric, piezoelectric, and flexoelectric materials, leveraging its advantage of integrating seamlessly with Computer-Aided Design (CAD) models to maintain exact geometrical fidelity. The CGAN method is highly efficient in generating models for piezoelectric and flexoelectric materials, specifically adapting to targeted design requirements and constraints. Then, the CGAN-IGA is adopted to calculate the electric potential of optimum models with different parameters to accelerate uncertainty quantification processes. The accuracy and feasibility of this method are verified through numerical experiments presented herein.},
DOI = {10.32604/cmes.2024.052203}
}