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Algorithms
Special Issues
AI and Computational Methods in Engineering and Science
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AI and Computational Methods in Engineering and Science

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

Deadline for manuscript submissions: 1 July 2025 | Viewed by 4414

Special Issue Editors

Prof. Dr. Dunhui Xiao

E-Mail Website
Guest Editor
School of Mathematical Sciences, Tongji University, Shanghai 200092, China
Interests: AI; machine learning; model reduction; finite element analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuai Li

E-Mail Website
Guest Editor
Faculty of Information Technology and Electrical Engineering, University of Oulu, 90570 Oulu, Finland
Interests: robotic manipulation; autonomous manufacturing; multi-robot coordination; intelligent control and optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI methods have shown great potential in many fields. Computational simulation or modeling is also a very important area and plays an important role in the environment, ecology, science and engineering. It provides a fantastic tool to help us to understand the world, and it is vital to apply the powerful AI to computational modeling or combine AI and modeling to help us to understand the world better.

This Special Issue welcomes original research articles, reviews, and case studies that explore the diverse applications of AI and computational methods in engineering and science. Contributions may cover a wide range of topics, including, but not limited to, the following:

  • Machine learning and deep learning algorithms for engineering and scientific modeling;
  • Intelligent systems and decision support in engineering and scientific processes;
  • Optimization techniques and evolutionary algorithms for engineering design and problem-solving;
  • Data-driven approaches for predictive modeling, anomaly detection, and fault diagnosis;
  • Simulation and modeling techniques enhanced by AI and computational methods;
  • Big data analytics and data mining in engineering and scientific domains;
  • Integration of AI with Internet of Things (IoT) and cyber–physical systems;
  • AI-enabled robotics and automation in engineering and scientific applications;
  • Computational intelligence in renewable energy systems, environmental sciences, and sustainability;
  • AI-driven image processing, computer vision, and pattern recognition in engineering and science.

This Special Issue will provide a platform for researchers, academics, and industry professionals to share their latest findings, methodologies, and real-world applications in the field of AI and computational methods within engineering and science. We encourage both theoretical and practical contributions that demonstrate the potential and impact of AI-driven approaches to address complex engineering and scientific challenges.

Prof. Dr. Dunhui Xiao
Prof. Dr. Shuai Li
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

  • machine learning
  • deep learning
  • AI applications
  • AI applications in environment
  • AI in ecology
  • AI in science and engineering

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  • 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.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

30 pages, 4061 KiB  
Article
Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models
by Jochen Garcke and Steffen Ruttscheidt
Algorithms 2025, 18(1), 40; https://doi.org/10.3390/a18010040 - 12 Jan 2025
Viewed by 353
Abstract
We present a finite difference method working on sparse grids to solve higher dimensional heterogeneous agent models. If one wants to solve the arising Hamilton–Jacobi–Bellman equation on a standard full grid, one faces the problem that the number of grid points grows exponentially [...] Read more.
We present a finite difference method working on sparse grids to solve higher dimensional heterogeneous agent models. If one wants to solve the arising Hamilton–Jacobi–Bellman equation on a standard full grid, one faces the problem that the number of grid points grows exponentially with the number of dimensions. Discretizations on sparse grids only involve O(N(logN)d−1) degrees of freedom in comparison to the O(Nd) degrees of freedom of conventional methods, where N denotes the number of grid points in one coordinate direction and d is the dimension of the problem. While one can show convergence for the used finite difference method on full grids by using the theory introduced by Barles and Souganidis, we explain why one cannot simply use their results for sparse grids. Our numerical studies show that our method converges to the full grid solution for a two-dimensional model. We analyze the convergence behavior for higher dimensional models and experiment with different sparse grid adaptivity types. Full article
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science)
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34 pages, 2158 KiB  
Article
Hybrid Empirical and Variational Mode Decomposition of Vibratory Signals
by Eduardo Esquivel-Cruz, Francisco Beltran-Carbajal, Ivan Rivas-Cambero, José Humberto Arroyo-Núñez, Ruben Tapia-Olvera and Daniel Guillen
Algorithms 2025, 18(1), 25; https://doi.org/10.3390/a18010025 - 5 Jan 2025
Viewed by 371
Abstract
Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of [...] Read more.
Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of very similar frequencies and mode mixing. In this context, a hybrid strategy to estimate harmonic vibration modes in weakly damped, multi-degree-of-freedom vibrating mechanical systems by combining Empirical Mode Decomposition and Variational Mode Decomposition is described. In this way, this hybrid approach leverages the detection of mode mixing based on the analysis of intrinsic mode functions through Empirical Mode Decomposition to determine the number of components to be estimated and thus provide greater information for Variational Mode Decomposition. The computational time and dependency on a predefined number of modes are significantly reduced by providing crucial information about the approximate number of vibratory components, enabling a more precise estimation with Variational Mode Decomposition. This hybrid strategy is employed to compute unknown natural frequencies of vibrating systems using output measurement signals. The algorithm for this hybrid strategy is presented, along with a comparison to conventional techniques such as Empirical Mode Decomposition, Variational Mode Decomposition, and the Fast Fourier Transform. Through several case studies involving multi-degree-of-freedom vibrating systems, the superior and satisfactory performance of the hybrid method is demonstrated. Additionally, the advantages of the hybrid approach in terms of computational efficiency and accuracy in signal decomposition are highlighted. Full article
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science)
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20 pages, 1224 KiB  
Article
A New Generalized Chebyshev Matrix Algorithm for Solving Second-Order and Telegraph Partial Differential Equations
by Waleed Mohamed Abd-Elhameed, Ramy M. Hafez, Anna Napoli and Ahmed Gamal Atta
Algorithms 2025, 18(1), 2; https://doi.org/10.3390/a18010002 - 26 Dec 2024
Viewed by 437
Abstract
This article proposes numerical algorithms for solving second-order and telegraph linear partial differential equations using a matrix approach that employs certain generalized Chebyshev polynomials as basis functions. This approach uses the operational matrix of derivatives of the generalized Chebyshev polynomials and applies the [...] Read more.
This article proposes numerical algorithms for solving second-order and telegraph linear partial differential equations using a matrix approach that employs certain generalized Chebyshev polynomials as basis functions. This approach uses the operational matrix of derivatives of the generalized Chebyshev polynomials and applies the collocation method to convert the equations with their underlying conditions into algebraic systems of equations that can be numerically treated. The convergence and error bounds are examined deeply. Some numerical examples are shown to demonstrate the efficiency and applicability of the proposed algorithms. Full article
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science)
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16 pages, 7254 KiB  
Article
Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN
by Xin Du, Jun Qi, Jiyi Kang, Zezhong Sun, Chunxin Wang and Jun Xie
Algorithms 2024, 17(11), 487; https://doi.org/10.3390/a17110487 - 1 Nov 2024
Viewed by 829
Abstract
Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. [...] Read more.
Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model’s discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy. Full article
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science)
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37 pages, 5770 KiB  
Article
A Review on Resource-Constrained Embedded Vision Systems-Based Tiny Machine Learning for Robotic Applications
by Miguel Beltrán-Escobar, Teresa E. Alarcón, Jesse Y. Rumbo-Morales, Sonia López, Gerardo Ortiz-Torres and Felipe D. J. Sorcia-Vázquez
Algorithms 2024, 17(11), 476; https://doi.org/10.3390/a17110476 - 24 Oct 2024
Viewed by 1956
Abstract
The evolution of low-cost embedded systems is growing exponentially; likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algorithms. We review the state-of-the-art strategies available for Tiny Machine Learning (TinyML) implementation to provide [...] Read more.
The evolution of low-cost embedded systems is growing exponentially; likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algorithms. We review the state-of-the-art strategies available for Tiny Machine Learning (TinyML) implementation to provide a complete overview using various existing embedded vision and control systems. Our discussion divides the article into four critical aspects that high-cost and low-cost embedded systems must include to execute real-time control and image processing tasks, applying TinyML techniques: Hardware Architecture, Vision System, Power Consumption, and Embedded Software Platform development environment. The advantages and disadvantages of the reviewed systems are presented. Subsequently, the perspectives of them for the next ten years are present. A basic TinyML implementation for embedded vision application using three low-cost embedded systems, Raspberry Pi Pico, ESP32, and Arduino Nano 33 BLE Sense, is presented for performance analysis. Full article
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science)
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