Please find attached the Call for Papers for the Special Collection Advances in Efficient Methods in Random Fields Modeling and Analysis.
Guest Editors
- Zhenhao Zhang, Changsha University of Science & Technology, zhangzhenhao@csust.edu.cn
- De-Cheng Feng, Southeast University, dcfeng@seu.edu.cn
- You Dong, The Hong Kong Polytechnic University, you.dong@polyu.edu.hk
- Emilio Bastidas-Arteaga, La Rochelle University, ebastida@univ-lr.fr
Aims & Scope
Spatial and temporal variability widely exists in practical engineering and has a significant influence on structural performance. Generally, it is modeled by the random field/process methods which typically transfer the field into a set of random variables, then it can be implemented in conventional uncertainty analysis framework. Efficient random field modeling and analysis usually involves three aspects, the adopted mathematical representation method, the accurate reflection of the geometric correlations, and the effective sampling of the discretized random variables. With the development of probabilistic mechanics and random process theory, novel methods are developed for efficient random field modeling and convenient uncertainty analysis of structures involving random field properties. Besides, the AI-inspired data-driven approaches bring new insights for resolving the traditional difficulties of random field analysis, e.g., correlation relation identification, surrogate models, dimension reduction methods, etc. This special collection aims to gather contributions presenting the recent advances in efficient random field modeling, analysis, and applications.