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Advances in Computational and Applied Mechanics (SACAM)
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Advances in Computational and Applied Mechanics (SACAM)

A special issue of Mathematical and Computational Applications (ISSN 2297-8747). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 6684

Special Issue Editors

Prof. Dr. Mohsen Sharifpur

E-Mail Website
Guest Editor
Thermofluids Division, School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Braamfontein, Johannesburg 2000, South Africa
Interests: heat transfer; CFD; multi-phase flow; nanofluids
Special Issues, Collections and Topics in MDPI journals
Dr. Philip Loveday

E-Mail Website
Guest Editor
Applied Mechanics Division, School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Braamfontein, Johannesburg 2000, South Africa
Interests: vibration; acoustics & ultrasound; guided wave ultrasound for NDE & SHM; piezoelectric transducers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational and applied mechanics play a pivotal role in solving complex engineering and scientific problems by integrating mathematical modeling, numerical simulations, and experimental validation. The rapid evolution of computational methods has enabled significant advancements in understanding fluid flow, structural behavior, heat and mass transfer, and multi-physics interactions across various disciplines. These advancements have led to innovations in diverse fields such as aerospace, biomedical engineering, materials science, energy systems, and nanotechnology.

This Special Issue will provide a platform for researchers and practitioners to present cutting-edge developments in computational and applied mechanics, fostering interdisciplinary collaboration and knowledge dissemination. By addressing emerging challenges and exploring novel methodologies, this Special Issue will contribute to the continuous improvement of computational techniques and their real-world applications.

The primary objectives of this special issue are as follows:

  1. Showcase Recent Advances: Present state-of-the-art research in computational and applied mechanics, including innovative mathematical models, numerical methods, and computational tools;
  2. Bridge Theory and Application: Promote studies that not only develop theoretical models but also validate them through experiments or real-world applications;
  3. Explore Multi-Disciplinary Approaches: Encourage contributions that integrate mechanics with artificial intelligence, optimization techniques, machine learning, and data-driven models;
  4. Address Emerging Challenges: Investigate new computational techniques to tackle complex problems in fluid mechanics, solid mechanics, heat transfer, biomechanics, and material science;
  5. Support Sustainable and Energy-Efficient Solutions: Highlight research that contributes to sustainable energy, efficient material usage, and environmentally friendly engineering solutions;
  6. Disseminate High-Quality Research: Provide a valuable resource for researchers, industry professionals, and academicians seeking the latest developments in computational and applied mechanics.

Prof. Dr. Mohsen Sharifpur
Dr. Philip Loveday
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. Mathematical and Computational Applications 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 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

  • computational fluid dynamics (CFD)
  • applied mechanics
  • finite element method (FEM) applications
  • multiphysics and multiscale modeling
  • heat transfer and thermal fluid systems
  • nanofluids and microfluidics
  • structural mechanics and material modeling
  • optimization and machine learning in engineering
  • biomechanics and bioengineering applications
  • mathematical methods in mechanics

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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.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • 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 (3 papers)

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Research

22 pages, 102250 KB  
Article
An Improved Method for 3D Style Transfer of Cliff Carvings Based on Gaussian Splatting
by Yang Li, He Ren, Yacong Li, Dong Sui and Maozu Guo
Math. Comput. Appl. 2026, 31(2), 47; https://doi.org/10.3390/mca31020047 - 11 Mar 2026
Viewed by 409
Abstract
Cliff carvings, as significant art forms bearing historical, cultural, and religious connotations, face dual threats from natural weathering and human-induced damage. Their protection and restoration of the artistic style present pressing challenges. In recent years, the rapid advancement of digital technologies has offered [...] Read more.
Cliff carvings, as significant art forms bearing historical, cultural, and religious connotations, face dual threats from natural weathering and human-induced damage. Their protection and restoration of the artistic style present pressing challenges. In recent years, the rapid advancement of digital technologies has offered new opportunities for preserving and reproducing cultural heritage. Particularly, 3D style transfer techniques are emerging as crucial tools for digital safeguarding. The advantages of three-dimensional style transfer in cultural heritage applications include dynamic stylized rendering, simulation of styles from multiple historical periods, alternative modes of exhibition, and facilitating a paradigm shift in conservation practices from static digital archiving to dynamic revitalization. This study proposes a novel 3D stylization method for cliff carvings by integrating 3D Gaussian Splatting (3DGS) and Nearest Neighbor Feature Matching (NNFM) loss metric. The method represents ancient cliff carvings as a set of optimizable 3D Gaussians representation, enabling efficient capture and processing of complex geometric structures and rich textural details. By integrating the textural and geometric characteristics of the target artistic style, 3DGS facilitates high-quality transfer of diverse artistic styles while effectively preserving the original intricate details of the carvings. Additionally, we employ the NNFM loss function to transfer 2D visual details into 3D representations while maintaining multi-perspective style consistency. Experimental results demonstrate that the proposed method exhibits significant advantages in texture fidelity, style consistency, and rendering efficiency. This research showcases the potential of our model for the digital preservation and presentation of cliff-carved cultural heritage, offering an innovative technological approach with theoretical value and practical significance. Full article
(This article belongs to the Special Issue Advances in Computational and Applied Mechanics (SACAM))
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27 pages, 2521 KB  
Article
IoTToe: Monitoring Foot Angle Variability for Health Management and Safety
by Ata Jahangir Moshayedi, Zeashan Khan, Zhonghua Wang and Mehran Emadi Andani
Math. Comput. Appl. 2026, 31(1), 13; https://doi.org/10.3390/mca31010013 - 16 Jan 2026
Viewed by 767
Abstract
Toe-in (inward) and toe-out (outward) foot alignments significantly affect gait, posture, and joint stress, causing issues like abnormal gait, joint strain, and foot conditions such as plantar fasciitis and high arches. Addressing these alignments is crucial for improving mobility and comfort. This study [...] Read more.
Toe-in (inward) and toe-out (outward) foot alignments significantly affect gait, posture, and joint stress, causing issues like abnormal gait, joint strain, and foot conditions such as plantar fasciitis and high arches. Addressing these alignments is crucial for improving mobility and comfort. This study introduces IoTToe, a wearable IoT device designed to detect and monitor gait patterns by using six ADXL345 sensors positioned on the foot, allowing healthcare providers to remotely monitor alignment via a webpage, reducing the need for physical tests. Tested on 45 participants aged 20–25 years with diverse BMIs, IoTToe proved suitable for both children and adults, supporting therapy and diagnostics. Statistical tests, including ICC, DFA, and ANOVA, confirmed the device’s effectiveness in detecting gait and postural control differences between legs. Gait variability results indicated that left leg showed more adaptability (DFA close to 0.5), compared to the right leg which was found more consistent (DFA close to 1). Postural control showed stable and agile standing with values between 0.5 and 1. Sensor combinations revealed that removing sensor B (on the gastrocnemius muscle) did not affect data quality. Moreover, taller individuals displayed smaller ankle angle changes, highlighting challenges in balance and upper body stability. IoTToe offers accurate data collection, reliability, portability, and significant potential for gait monitoring and injury prevention. Future studies would expand participation, especially among women and those with alignment issues, to enhance the system’s applicability for foot health management, safety and rehabilitation, further supporting telemetric applications in healthcare. Full article
(This article belongs to the Special Issue Advances in Computational and Applied Mechanics (SACAM))
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21 pages, 7862 KB  
Article
Physics-Informed Neural Network for Nonlinear Bending Analysis of Nano-Beams: A Systematic Hyperparameter Optimization
by Saba Sadat Mirsadeghi Esfahani, Ali Fallah and Mohammad Mohammadi Aghdam
Math. Comput. Appl. 2025, 30(4), 72; https://doi.org/10.3390/mca30040072 - 14 Jul 2025
Cited by 2 | Viewed by 4629
Abstract
This paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton’s principle, incorporating nonlocal strain gradient theory, and based on Euler–Bernoulli beam theory. In [...] Read more.
This paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton’s principle, incorporating nonlocal strain gradient theory, and based on Euler–Bernoulli beam theory. In the PINN method, the solution is approximated by a deep neural network, with network parameters determined by minimizing a loss function that consists of the governing equation and boundary conditions. Despite numerous reports demonstrating the applicability of the PINN method for solving various engineering problems, tuning the network hyperparameters remains challenging. In this study, a systematic approach is employed to fine-tune the hyperparameters using hyperparameter optimization (HPO) via Gaussian process-based Bayesian optimization. Comparison of the PINN results with available reference solutions shows that the PINN, with the optimized parameters, produces results with high accuracy. Finally, the impacts of boundary conditions, different loads, and the influence of nonlocal strain gradient parameters on the bending behavior of nano-beams are investigated. Full article
(This article belongs to the Special Issue Advances in Computational and Applied Mechanics (SACAM))
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