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Algorithms
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Artificial Intelligence in Modeling and Simulation (2nd Edition)
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Artificial Intelligence in Modeling and Simulation (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 4714

Special Issue Editors

Dr. Nuno Fachada

E-Mail Website
Guest Editor
COPELABS, Lusophone University, 1700-097 Lisboa, Portugal
Interests: computer science; machine learning; artificial intelligence; modeling and simulation; high performance computing
Special Issues, Collections and Topics in MDPI journals
Dr. Nuno David

E-Mail Website
Guest Editor
Department of Information Sciences and Technologies, University Instituto de Lisboa, 1649-026 Lisboa, Portugal
Interests: simulation; agent-based models; computer ethics; privacy and data protection; philosophy of technology; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modeling and Simulation (M&S) uses various methods, including continuous, discrete-event, and agent-based approaches, to represent and analyze real-world systems. It is an essential tool in science and engineering, offering alternatives to physical experimentation, which can be costly, time-consuming, or impractical in cases such as inaccessible environments or systems still under development. M&S is widely applied in fields like physics, social sciences, medicine, defense, transportation, chemistry, ecology, and biology, where it helps to predict and understand complex system behaviors without direct experimentation.

Artificial Intelligence (AI) has become increasingly important in M&S, enabling the development of more sophisticated and adaptive models. AI refers to systems that can perceive their environment and perform tasks to achieve specific goals, often replicating human-like intelligence. In M&S, AI is integrated in several ways, for example by embedding AI within simulation models or by using AI to develop and optimize these models. When AI is a component of the real system—such as in autonomous vehicles, smart factories, or social networks—it must be accurately reflected in the simulation models. In other cases, simulations that aim to mimic natural intelligence rely on AI techniques to achieve this.

AI techniques, particularly machine learning, enhance M&S by optimizing and fine-tuning models. These methods enable models to be trained using real system data, improving accuracy and reliability. AI also aids in synchronizing models with real-time data, improving verification and validation processes. One significant advancement is the creation of metamodels, which predict model outcomes across various parameters without requiring full simulations, significantly reducing computational time. This is particularly useful in complex simulations, such as agent-based or multiscale models, where exploring all parameter combinations is computationally intensive.

The interaction between AI and M&S is mutually beneficial. Simulation models can generate synthetic data to train AI systems, which can then be applied in practical contexts. This synergy not only improves the performance of simulations but also drives the advancement of AI methods.

This Special Issue seeks contributions that examine the relationship between AI and M&S, focusing on the following: (1) the application of AI techniques, including machine learning, in M&S across various domains; (2) the development and optimization of simulation models using AI, with a focus on verification and validation; (3) the creation of AI-driven metamodels to enhance simulation efficiency; and (4) the role of simulation-generated data in training AI systems.

We invite submissions addressing these topics from theoretical, algorithmic, and practical perspectives across academic and industrial applications.

Dr. Nuno Fachada
Dr. Nuno David
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • modeling and simulation
  • artificial intelligence
  • machine learning
  • verification and validation
  • metamodeling

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Related Special Issue

Published Papers (3 papers)

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Research

23 pages, 2398 KiB  
Article
Implementation of an Intelligent Controller Based on Neural Networks for the Simulation of Pressure Swing Adsorption Systems
by Moises Ramos-Martinez, Jorge A. Brizuela-Mendoza, Carlos A. Torres-Cantero, Gerardo Ortiz-Torres, Felipe D. J. Sorcia-Vázquez, Mario A. Juarez, Jair de Jesús Cambrón Navarrete, Juan Carlos Mixteco-Sánchez, Mayra G. Mena-Enriquez, Rafael Murrieta Yescas and Jesse Y. Rumbo-Morales
Algorithms 2025, 18(4), 215; https://doi.org/10.3390/a18040215 - 10 Apr 2025
Viewed by 196
Abstract
Biohydrogen has been identified as an attractive renewable energy carrier due to its high energy density and green production from biomass and organic wastes. Efficient biohydrogen production is a challenge that demands precise control of process parameters. Regulation and optimization of biohydrogen production [...] Read more.
Biohydrogen has been identified as an attractive renewable energy carrier due to its high energy density and green production from biomass and organic wastes. Efficient biohydrogen production is a challenge that demands precise control of process parameters. Regulation and optimization of biohydrogen production through advanced approaches are therefore necessary to improve its industrial viability. This study introduces an innovative proposal for controlling the Pressure Swing Adsorption (PSA) process by employing a neural network-based controller derived from a PID control framework. The neural network was trained using input–output data, enabling it to maintain biohydrogen production purity at approximately 99%. The proposed neural network effectively simulates the dynamics of the PSA model, which is traditionally controlled using a PID controller. The results demonstrate exceptional performance and strong robustness against disturbances. Specifically, the neural network enables precise tracking of the desired trajectory and effective attenuation of disturbances, achieving a biohydrogen purity level with a molar fraction of 0.99. Full article
(This article belongs to the Special Issue Artificial Intelligence in Modeling and Simulation (2nd Edition))
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26 pages, 1482 KiB  
Article
Anomaly Detection and Root Cause Analysis for Energy Consumption of Medium and Heavy Plate: A Novel Method Based on Bayesian Neural Network with Adam Variational Inference
by Qiang Guo, Fenghe Li, Hengwen Liu and Jin Guo
Algorithms 2025, 18(1), 11; https://doi.org/10.3390/a18010011 - 2 Jan 2025
Viewed by 1002
Abstract
Anomaly detection and root cause analysis of energy consumption not only optimize energy use and improve equipment reliability but also contribute to green and low-carbon development. This paper proposes a comprehensive diagnostic framework for detecting anomalies, conducting causal analysis, and tracing root causes [...] Read more.
Anomaly detection and root cause analysis of energy consumption not only optimize energy use and improve equipment reliability but also contribute to green and low-carbon development. This paper proposes a comprehensive diagnostic framework for detecting anomalies, conducting causal analysis, and tracing root causes of energy consumption in medium and heavy plate manufacturing, integrating process mechanisms, expert knowledge, and industrial big data. First, a two-stage anomaly detection method based on box plot analysis is developed to identify energy consumption irregularities. Next, a weighted Granger causality analysis method based on LSTM is introduced, which effectively captures the nonlinear and temporal relationships of process variables, enabling the identification of abnormal causal pathways. Finally, a root cause tracing algorithm using an Adam-based variational inference Bayesian neural network is proposed to pinpoint the underlying factors responsible for the anomalies. Experimental results validate the effectiveness of the proposed methods. Full article
(This article belongs to the Special Issue Artificial Intelligence in Modeling and Simulation (2nd Edition))
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40 pages, 11208 KiB  
Article
Mapping the Frontier: A Bibliometric Analysis of Artificial Intelligence Applications in Local and Regional Studies
by Camelia Delcea, Ionuț Nica, Ștefan Ionescu, Bianca Cibu and Horațiu Țibrea
Algorithms 2024, 17(9), 418; https://doi.org/10.3390/a17090418 - 20 Sep 2024
Cited by 2 | Viewed by 2920
Abstract
This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from [...] Read more.
This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development. Full article
(This article belongs to the Special Issue Artificial Intelligence in Modeling and Simulation (2nd Edition))
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