Synergy between Multiphysics/Multiscale Modeling and Machine Learning
A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".
Deadline for manuscript submissions: 15 December 2024 | Viewed by 858
Special Issue Editor
Special Issue Information
Dear Colleagues,
The proposed special issue aims to explore recent advancements in mechanics and applied mathematics within the dynamic research area of multi-scale modeling and computations applied to various domains, including solids, fluids, structures, systems, and multi-physics problems. A key objective of this thematic issue is to investigate the synergies between traditional multi-physics/multi-scale methodologies and model construction techniques utilizing machine learning algorithms. Drawing upon the expertise of computational mechanics specialists, this special issue endeavors to identify efficient reduced models developed with appropriate assumptions and kinematic constraints. Additionally, classical structural models are highlighted for their utility in constructing relevant multi-scale and multi-physics models. Concurrently, there is a burgeoning interest in leveraging Artificial Intelligence (AI) and statistical data analysis algorithms, including machine learning approaches, for mechanics modeling. How can these approaches be harmonized? Can they leverage each other's advancements? Can model construction proficiency be simplified to the application of AI algorithms?
Therefore, the special issue also welcomes submissions from the fields of aerospace engineering, civil engineering, mechanical engineering, materials science, and applied mathematics for the design and analysis of numerical algorithms.
Prof. Dr. Adnan Ibrahimbegovic
Guest Editor
Manuscript Submission Information
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Keywords
- heterogeneous materials
- complex structures
- multibody systems
- machine learning
- biomechanics
- material and structure failures
- adaptive modeling
- mechanics of porous media
- fluid-structure interaction
- multi-phase flows
- wave propagation
- stochastic processes
- uncertainty propagation
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