Computer Methods in Applied Mechanics and Engineering
Stochastic data-driven modeling of heterogeneous materials across multiple length scales
摘要截稿:
全文截稿: 2024-05-31
影响因子: 5.763
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 1区
• 小类 : 工程:综合 - 1区
• 小类 : 数学跨学科应用 - 1区
• 小类 : 力学 - 1区
Overview
In recent years, integrating stochastic methods into a multiscale framework or developing multiscale modeling in a stochastic setting for uncertainty quantification (UQ) and reliability analysis of heterogeneous materials has become an emerging research frontier. Translation of deterministic multiscale methods into corresponding stochastic versions requires the development of highly efficient algorithms to deal with the “curse of dimension” problem and the knowledge of multiscale characteristics of complex material systems. Specifically, the emergence of new advanced engineered materials makes the need for accurate uncertainty modeling across multiple length scales imperative. Equally important is to ensure the link between uncertainty models and physical realities by connecting UQ methods to material science and experimental mechanics in a data-driven framework. Machine learning models could also serve as surrogates to enhance the computational efficiency in solving high-dimensional problems.
The objectives of the Special Issue on “Stochastic data-driven modeling of heterogeneous materials across multiple length scales” are to present the state-of-the-art methods in this field, highlight recent research, and identify future trends that may be used in the engineering profession. In this respect, suitable topics for papers in the Special Issue may include:
Random field modeling of heterogeneous media
Efficient simulation of random microstructure/morphology
Stochastic modeling of fracture and damage
Homogenization of materials with random microstructure
Finite element solution of multiscale stochastic partial differential equations
Stochastic finite element (SFE) analysis of composite materials and structures
Efficient algorithms to accelerate the SFE solution of multiscale problems
Methods for improving the efficiency of Monte Carlo simulation
Large-scale applications
Design / Optimization of composite materials and structures considering uncertainty
Machine learning techniques for efficient material modeling
Data-driven material modeling
Guest editors:
Prof. George Stefanou
Affiliation: Aristotle University of Thessaloniki, Thessaloniki, Greece
Prof. David Moens
Affiliation: KU Leuven, Leuven, Belgium
Manuscript submission information:
Guest Editor Invitation Only
Open for Submission: from 01-Feb-2024 to 31-May-2024