Machine Learning Techniques in Materials Science and Engineering
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".
Deadline for manuscript submissions: closed (10 June 2024) | Viewed by 19442
Special Issue Editors
Interests: Artificial Intelligence; bioinformatics; building materials; computational mechanics; machine learning
Special Issues, Collections and Topics in MDPI journals
2. College of Engineering, American University of Iraq, Sulaimani, Kurdistan region, Sulaimani-Kirkuk Rd, Sulaymaniyah 46001, Iraq
Interests: cement; concrete; soil mechanics; rock mechanics; sustainability; modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Numerous modeling approaches using non-conventional adsorbents have been used to develop suitable and more effective adsorbents to eliminate and optimize experimental lab work. Since soft computing techniques are needed in composite materials engineering, they could be used in different phases of materials engineering. It is especially related to functional analysis, design, testing, prediction, and optimization. Neural networks (NNs), fuzzy logic, and evolutionary and classification algorithms are the most popular soft-computing techniques.
Articles submitted to this journal can also be concerned about the most significant recent developments in computational and numerical methods and their applications in structural engineering and Materials Engineering, including nano-sized and smart materials. We invite researchers to contribute original research articles and review articles that will stimulate the continuing research effort on applications of the soft computing approaches to model structural engineering and materials problems.
Prof. Dr. Panagiotis G. Asteris
Dr. Ahmed Salih Mohammed
Guest Editors
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Keywords
- artificial neural networks (ANNs)
- computational biology/bioinformatics
- computational science and engineering
- evolutionary multimodal optimization
- forecasting models
- fuzzy set theory and hybrid fuzzy models
- genetic algorithm and genetic programming
- heuristic models
- hybrid intelligent systems
- image processing and computer vision
- machine learning techniques
- multicriteria decision making (MCDM)
- multiexpression programming
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