Machine Learning in Computational Mechanics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 283
Special Issue Editor
Interests: computational mechanics; solids and structures; fluid–structure interaction; finite element technology; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Computational Mechanics employs numerical models that require time- and resource-consuming simulations to replicate physical phenomena, usually with the final goal of optimizing the model with respect to desired purposes and functionalities.
Machine learning (ML) has emerged as a powerful tool in Computational Mechanics, impacting all of its areas, such as Structural/Solid Mechanics, Fluid Mechanics, Fluid–Structure Interaction, etc. Undoubtedly, pioneering work has demonstrated that ML may provide solutions to governing systems of equations with comparable accuracy to those obtained using direct numerical methods, but with significantly faster computational speed. In this context, ML algorithms may play a pivotal role by facilitating the creation of models that closely approximate the outcomes of simulations, expediting the identification of high-performing configurations.
This Special Issue aims to present cutting-edge work on novel ML technologies that advance the understanding of Computational Mechanics problems. In particular, contributions on the following topics are sought:
- ML-enhanced simulations in Computational Mechanics.
- Development of predictive tools for Computational Mechanics problems by using data-driven techniques.
- The use of ML for pattern recognition in Computational Mechanics problems.
- Deep Learning and Physics-Informed Neural Networks for Computational Mechanics.
- Novel algorithms and theoretical developments on the application of ML to Computational Mechanics.
- Development of low-order models using ML.
Dr. Hugo Santos
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- computational mechanics
- numerical methods
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