Advances in Methods of Computational Modeling in Engineering Sciences, a Special Issue in Memory of Professor Satya Atluri

Submission Deadline: 31 December 2024 Submit to Special Issue

Guest Editors

Summary

Computational modeling has become a cornerstone in engineering sciences, enabling engineers to simulate and predict the behavior of complex systems with remarkable accuracy. From Finite Element Analysis and Computational Fluid Dynamics to multi-scale and multi-physics modeling, these techniques have found applications in diverse domains. Moreover, the integration of machine learning and artificial intelligence with computational modeling has opened up new avenues for research and innovation. These intelligent techniques facilitate data-driven model development, optimization, and decision-making processes, enabling engineers to harness the vast amounts of available data to enhance the accuracy and efficiency of simulations. Additionally, the emergence of high-performance computing has accelerated the pace of computational modeling by enabling large-scale simulations and parallel processing.

The special issue aims to foster interdisciplinary collaboration and knowledge exchange among researchers and practitioners from various engineering fields. It seeks to address topics such as computational methods for engineering design, uncertainty quantification, simulation-driven engineering, and virtual prototyping, etc. Furthermore, it encourages the exploration of computational modeling applications in emerging areas, including aerospace, automotive engineering, biomedical engineering, and sustainable engineering practices.

You are invited to submit original research papers or review papers to this special issue that showcase the latest advancements in computational modeling methodologies, highlighting their potential to address real-world engineering challenges. Topics of interest include, but are not restricted to:

• Physics-based, data-driven, and hybrid simulation in engineering sciences

• Model calibration and updating

• Multi-scale and multi-physics modeling approaches

• Multidisciplinary modeling for engineering design and analysis

• Machine learning and artificial intelligence in computational modeling

• High-performance computing for large-scale simulations

• Validation and verification of computational models

• Uncertainty quantification and sensitivity analysis in computational modeling

• Computational modeling for risk analysis and reliability engineering

• Case studies showcasing successful applications of computational modeling in engineering sciences

• Digital twin and digital engineering

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Published Papers