Nonlinear and Evolutionary Optimization in Materials and Engineering
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".
Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 2313
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue "Nonlinear and Evolutionary Optimization in Materials and Engineering" delves into the intricate fusion of evolutionary algorithms in the realm of materials science and engineering. This Special Issue aims to provide an in-depth examination of the symbiotic relationship between evolutionary optimization techniques and the optimization challenges inherent in material properties, processing methodologies, and engineering structures. Artificial evolutionary methods such as genetic algorithms and other population-based nonlinear optimization techniques, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than via physical experiments alone. Machine learning models can augment experimental measurements of material fitness to accelerate the identification of useful and novel materials in vast material compositions or property spaces.
This Special Issue welcomes novel research and applications where evolutionary algorithms, nonlinear optimization, artificial intelligence, and machine learning are employed to optimize material properties, manufacturing processes, and engineering designs. All the works involved in the application of evolutionary algorithms, artificial intelligence, and machine learning to material science and engineering are welcome.
Dr. Amit Banerjee
Guest Editor
Manuscript Submission Information
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Keywords
- evolutionary algorithms
- evolutionary optimization
- nonlinear optimization
- artificial intelligence
- machine learning
- evolutionary computation
- deep learning
- material science
- computational materials design
- smart materials
- additive manufacturing
- smart manufacturing processes
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