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Advances in Machine Learning and Computational Mechanics

Guest Editors:
Beatriz Moya, PhD, CNRS@CREATE, Singapore
Alberto Badías, PhD, Universidad Politécnica de Madrid, Spain
Professor Chady Ghnatios, PhD, Arts et Métiers institute of technology, France
Giovanni Stabile, PhD, University of Urbino Carlo Bo, Italy
Olga Mula, PhD, Eindhoven University of Technology, Netherlands

Submission Status: Open   |   Submission Deadline: 15 August 2024

Advanced Modeling and Simulation in Engineering Sciences is calling for submissions to our new Collection on "Advances in Machine Learning and Computational Mechanics". 

Deep learning tools in computational mechanics hold significant importance in current scientific and engineering research. The integration of both data-driven disciplines and computational mechanics is transforming the research landscape, influencing how we design, simulate, analyze and optimize complex systems. Merging knowledge coming from these two fields provides new insights for a deeper understanding of mechanics. These synergies appear in multiple industrial applications, from aerospace to material science and civil infrastructures, becoming a critical, and interdisciplinary, area of study. 

Although one of the most active fields is the so-called physics-informed machine learning, the use of these techniques with statistical foundation has had a great impact in other studies such as to learn patterns in massive datasets and unveil correlations in the available data. The final goal is always to highlight new insights, which improve the scientific understanding of mechanics, while facilitating optimization through surrogates. Moreover, inverse modeling becomes at hand through reducing the computational cost.

This Collection focuses on the latest advances in machine learning and deep learning for computational mechanics applications. The goal is to showcase recent advances in the development and understanding of coupled machine learning and physical modeling for complex physical systems, along with their applications across industrial domains.

Submissions aligned with the following topics are expected:

  • Advances of simulation for predicting complex behaviors of materials, structures, and dynamics
  • Real time simulation and control in multiscale analysis
  • Model order reduction coupled with machine learning applications
  • Model calibration, adaptation, and correction
  • Stochastic and uncertainty analysis in complex systems and data assimilation
  • Data-driven insights discovery

Submission Guidelines

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This Collection welcomes submission of Research Articles. 

Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system. During the submission process, under the section additional information, you will be asked whether you are submitting to a Collection/Thematic Series, please select "Advances in Machine Learning and Computational Mechanics" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.

  1. This paper explores the ability of physics-informed neural networks (PINNs) to solve forward and inverse problems of contact mechanics for small deformation elasticity. We deploy PINNs in a mixed-variable form...

    Authors: Tarik Sahin, Max von Danwitz and Alexander Popp
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:11
  2. Trajectory planning aims at computing an optimal trajectory through the minimization of a cost function. This paper considers four different scenarios: (i) the first concerns a given trajectory on which a cost...

    Authors: Chady Ghnatios, Daniele Di Lorenzo, Victor Champaney, Amine Ammar, Elias Cueto and Francisco Chinesta
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2024 11:10