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Modelling
Special Issues
Machine Learning and Artificial Intelligence in Modelling
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Machine Learning and Artificial Intelligence in Modelling

A special issue of Modelling (ISSN 2673-3951).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 2616

Special Issue Editors

Dr. Margarita Terziyska

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Guest Editor
Department of Mathematics, Physics and Information Technologies, University of Food Technologies, 4002 Plovdiv, Bulgaria
Interests: artificial intelligence; machine learning; neural networks; deep learning; fuzzy logic
Special Issues, Collections and Topics in MDPI journals
Dr. Miroslava Ivanova

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Guest Editor
Department of Informatics and Mathematics (DIM), Faculty of Economics, Trakia University, 6000 Stara Zagora, Bulgaria
Interests: mathematics; mathematical modeling; applied mathematics and statistics

Special Issue Information

Dear Colleagues,

In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have radically transformed how we build, validate, and apply models across science, engineering, and industry. These technologies offer powerful methods for capturing nonlinear dependencies, learning from heterogeneous and high-dimensional data, and modeling complex dynamic systems where traditional mathematical approaches often fall short.

This Special Issue aims to gather original research articles and reviews focused on the application of modern ML and AI methods in the context of engineering modeling and simulation. We welcome contributions on hybrid approaches that integrate domain knowledge with learning algorithms, interpretable models, and physics-informed machine learning techniques. In addition, the Special Issue is open to a broad spectrum of topics, including but not limited to:

  • Automated modeling and AutoML
  • Modeling with limited data and transfer learning
  • Explainable and trustworthy AI models
  • Probabilistic and Bayesian modeling
  • Spatio-temporal AI and time-dependent models
  • Multimodal modeling (e.g., combining text, images, sensors)
  • Neuro-symbolic and symbolic modeling approaches
  • Meta-modeling and higher-order model composition
  • AI-accelerated simulation and surrogate modeling
  • Embedded models and edge AI for real-time systems

The Special Issue encourages interdisciplinary contributions that demonstrate methodological innovation or application-driven insight within the scope of engineering sciences. Our goal is to foster a platform for knowledge exchange on the next generation of intelligent modeling paradigms that are shaping the future of engineering.

Dr. Margarita Terziyska
Dr. Miroslava Ivanova
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 250 words) can be sent to the Editorial Office for assessment.

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. Modelling 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 1200 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
  • artificial intelligence
  • hybrid modeling
  • physics-informed machine learning
  • interpretable AI
  • autoML and automated modeling
  • bayesian and probabilistic modeling
  • spatio-temporal models
  • edge AI and real-time systems
  • multimodal data fusion

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  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

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Research

22 pages, 4777 KB  
Article
Research on Automatic Recognition and Dimensional Quantification of Surface Cracks in Tunnels Based on Deep Learning
by Zhidan Liu, Xuqing Luo, Jiaqiang Yang, Zhenhua Zhang, Fan Yang and Pengyong Miao
Modelling 2026, 7(1), 4; https://doi.org/10.3390/modelling7010004 - 23 Dec 2025
Abstract
Cracks serve as a critical indicator of tunnel structural degradation. Manual inspections are difficult to meet engineering requirements due to their time-consuming and labor-intensive nature, high subjectivity, and significant error rates, while traditional image processing methods exhibit poor performance under complex backgrounds and [...] Read more.
Cracks serve as a critical indicator of tunnel structural degradation. Manual inspections are difficult to meet engineering requirements due to their time-consuming and labor-intensive nature, high subjectivity, and significant error rates, while traditional image processing methods exhibit poor performance under complex backgrounds and irregular crack morphologies. To address these limitations, this study developed a high-quality dataset of tunnel crack images and proposed an improved lightweight semantic segmentation network, LiteSqueezeSeg, to enable precise crack identification and quantification. The model was systematically trained and optimized using a dataset comprising 10,000 high-resolution images. Experimental results demonstrate that the proposed model achieves an overall accuracy of 95.15% in crack detection. Validation on real-world tunnel surface images indicates that the method effectively suppresses background noise interference and enables high-precision quantification of crack length, average width, and maximum width, with all relative errors maintained within 5%. Furthermore, an integrated intelligent detection system was developed based on the MATLAB (R2023b) platform, facilitating automated crack feature extraction and standardized defect grading. This system supports routine tunnel maintenance and safety assessment, substantially enhancing both inspection efficiency and evaluation accuracy. Through synergistic innovations in lightweight network architecture, accurate quantitative analysis, and standardized assessment protocols, this research establishes a comprehensive technical framework for tunnel crack detection and structural health evaluation, offering an efficient and reliable intelligent solution for tunnel condition monitoring. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Modelling)
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26 pages, 4037 KB  
Article
TE-G-SAGE: Explainable Edge-Aware Graph Neural Networks for Network Intrusion Detection
by Riko Luša, Damir Pintar and Mihaela Vranić
Modelling 2025, 6(4), 165; https://doi.org/10.3390/modelling6040165 - 12 Dec 2025
Viewed by 392
Abstract
Graph learning is well suited to modeling relationships among communicating entities in network intrusion detection. However, the resulting models are frequently difficult to interpret, in contrast to many classical approaches that offer more transparent reasoning. This work integrates SHapley Additive exPlanations with temporal, [...] Read more.
Graph learning is well suited to modeling relationships among communicating entities in network intrusion detection. However, the resulting models are frequently difficult to interpret, in contrast to many classical approaches that offer more transparent reasoning. This work integrates SHapley Additive exPlanations with temporal, edge-aware GNN based on GraphSAGE architecture to deliver an explainable, inductive intrusion detection model for NetFlow data named TE-G-SAGE. Using the NF-UNSW-NB15-v3 dataset, flow data are transformed into temporal communication graphs where flows are directed edges and endpoints are nodes. The model learns relational patterns across two-hop neighborhoods and achieves strong recall under chronological evaluation, outperforming a GCN baseline and recovering more attacks than a tuned XGBoost model. SHAP is adapted to graph inputs through a feature attribution on the two-hop computational subgraph, producing global and local explanations that align with analyst reasoning. The resulting attributions identify key discriminative features while revealing shared indicators that explain cross-class confusion. The research shows that temporal validation, inductive graph modeling, and Shapley-based attribution can be combined into a transparent, reproducible intrusion detection framework suited for operational use. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Modelling)
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25 pages, 5578 KB  
Article
Neural Network Approach for the Estimation of Quadrotor Aerodynamic and Inertial Parameters
by Alejandro Jimenez-Flores, Pablo A. Tellez-Belkotosky, Edmundo Javier Ollervides-Vazquez, Luis Arturo Reyes-Osorio, Luis Amezquita-Brooks and Octavio Garcia-Salazar
Modelling 2025, 6(4), 157; https://doi.org/10.3390/modelling6040157 - 30 Nov 2025
Viewed by 273
Abstract
The translational and rotational dynamics of quadrotor UAVs are commonly described by mathematical modeling where aerodynamic and inertial parameters are involved. Therefore, the importance of having accurate parameters in the model is critical for the correct performance of the UAV. In this paper, [...] Read more.
The translational and rotational dynamics of quadrotor UAVs are commonly described by mathematical modeling where aerodynamic and inertial parameters are involved. Therefore, the importance of having accurate parameters in the model is critical for the correct performance of the UAV. In this paper, Artificial Neural Networks (ANNs) are used to estimate the aerodynamic and inertial parameters corresponding to the mathematical model of a quadrotor. Thrust and torque coefficients from the rotor models and the quadrotor inertia matrix are estimated by proposing and training two different ANN models implementing the back-propagation algorithm, using both experimental and simulation data. The estimated parameters are then compared with the reference parameters by means of quadrotor attitude simulations, showing high accuracy in their behavior. The results have shown that the proposed ANN models can accurately estimate both the aerodynamic and inertial parameters of a quadrotor UAV model using both experimental and simulation data, thus contributing to increasing the tools available for parameter estimation. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Modelling)
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31 pages, 5909 KB  
Article
Machine Learning Approaches for Classification of Composite Materials
by Dmytro Tymoshchuk, Iryna Didych, Pavlo Maruschak, Oleh Yasniy, Andrii Mykytyshyn and Mykola Mytnyk
Modelling 2025, 6(4), 118; https://doi.org/10.3390/modelling6040118 - 1 Oct 2025
Cited by 1 | Viewed by 1252
Abstract
The paper presents a comparative analysis of various machine learning algorithms for the classification of epoxy composites reinforced with basalt fiber and modified with inorganic fillers. The classification is based on key thermophysical characteristics, in particular, the mass fraction of the filler, temperature, [...] Read more.
The paper presents a comparative analysis of various machine learning algorithms for the classification of epoxy composites reinforced with basalt fiber and modified with inorganic fillers. The classification is based on key thermophysical characteristics, in particular, the mass fraction of the filler, temperature, and thermal conductivity coefficient. A dataset of 16,056 interpolated samples was used to train and evaluate more than a dozen models. Among the tested algorithms, the MLP neural network model showed the highest accuracy of 99.7% and balanced classification metrics F1-measure and G-Mean. Ensemble methods, including XGBoost, CatBoost, ExtraTrees, and HistGradientBoosting, also showed high classification accuracy. To interpret the results of the MLP model, SHAP analysis was applied, which confirmed the predominant influence of the mass fraction of the filler on decision-making for all classes. The results of the study confirm the high effectiveness of machine learning methods for recognizing filler type in composite materials, as well as the potential of interpretable AI in materials science tasks. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Modelling)
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