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 27
Special Issue Editors
Interests: AI; machine learning; model reduction; finite element analysis
Special Issues, Collections and Topics in MDPI journals
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 **ao
Prof. Dr. Shuai Li
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at mdpi.longhoe.net 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
