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Applied Sciences
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Machine Learning in Multi-scale Modeling
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Machine Learning in Multi-scale Modeling

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 1326

Special Issue Editors

Dr. Yousef Heider

E-Mail Website
Guest Editor
Institute of Mechanics and Computational Mechanics (IBNM), Leibniz University Hannover, Appelstraße 9A, 30167 Hannover, Germany
Interests: multi-scale modeling; machine learning in engineering; porous media mechanics; fracture mechanics; continuum mechanics; phase-field modeling
Dr. Nikolaos Napoleon Vlassis

E-Mail Website
Guest Editor
Mechanical and Aerospace Engineering Rutgers, The State University of New Jersey, 98 Bread Road, A200 Piscataway, New Brunswick, NJ 08854–8058, USA
Interests: generative artificial intelligence; geometric learning; computational solid mechanics; machine learning in engineering

Special Issue Information

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.

Keywords

  • 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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

24 pages, 2716 KiB  
Article
A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
by Yousef Heider, Fadi Aldakheel and Wolfgang Ehlers
Appl. Sci. 2025, 15(5), 2589; https://doi.org/10.3390/app15052589 - 27 Feb 2025
Cited by 1 | Viewed by 613
Abstract
This work introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of μ-CT images of real microgeometries. The primary goal is to develop an efficient, machine learning (ML)-based method that overcomes the [...] Read more.
This work introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of μ-CT images of real microgeometries. The primary goal is to develop an efficient, machine learning (ML)-based method that overcomes the limitations of traditional permeability estimation techniques, which often rely on time-consuming experiments or computationally expensive fluid dynamics simulations. The novelty of this work lies in leveraging convolutional neural networks (CNNs) to predict pore-fluid flow behavior under deformation and anisotropic flow conditions. The approach utilizes binarized CT images of porous microstructures to predict the permeability tensor, a crucial parameter in continuum porous media flow modeling. The methodology involves four steps: (1) constructing a dataset of CT images from Bentheim sandstone at varying volumetric strain levels; (2) conducting pore-scale flow simulations using the lattice Boltzmann method (LBM) to obtain permeability data; (3) training the CNN model with processed CT images as inputs and permeability tensors as outputs; and (4) employing techniques like data augmentation to enhance model generalization. Examples demonstrate the CNN’s ability to accurately predict the permeability tensor in connection with the deformation state through the porosity parameter. A source code has been made available as open access. Full article
(This article belongs to the Special Issue Machine Learning in Multi-scale Modeling)
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