Machine Learning in Multi-scale Modeling

Dear Colleagues,

We are excited to announce a Special Issue on machine learning (ML) applications in multi-scale modeling for engineering and materials science. We invite you to submit original and high-quality research papers that focus on the following topics:

• Physics-informed ML for constitutive modeling in multi-scale structural and material systems;
• The use of artificial neural networks (ANNs) to predict effective material properties;
• Graph- and manifold-learning techniques in computational solid mechanics and material design;
• Supervised and unsupervised ANN methods, including reduced-order simulations in computational mechanics;
• The application of ANNs and ML-based optimization in the design of metamaterials relating to 3D-printing technologies;
• Generative AI and deep learning-aided techniques for the multi-scale modeling and inverse design of materials and structural systems, including multiphysics composites and porous metamaterials;
• Model-free approaches in computational mechanics;
• Causal discovery for interpretable modeling;
• Data-driven methods for solving partial differential equations (PDEs).

Dr. Yousef Heider
Dr. Nikolaos Napoleon Vlassis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• multi-scale modeling
• metamaterial design
• constitutive modeling
• inverse design
• data-driven simulations
• graph and manifold learning
• artificial neural networks
• generative AI
• model-free approaches
• causal discovery