Recent Computational Analysis and Simulation for Mathematical Soft Computing Modelling and Nanoscience: Optimisation Techniques

Dear Colleagues,

Due to its importance in the semiconductor industry, the field of heat transfer in nanostructured materials and its optimisation using data science has recently received more attention from researchers. In order to address the heat dissipation issue of chips with high power densities, which is currently hindering further advances, there is an urgent need to develop nanostructures with a high heat transfer efficiency. Additionally, this field of study contains extensive research with a major influence on microelectronics, thermal logic devices, and thermoelectric technologies. Nanostructures provide a vast "playground" for studying heat transmission. However, controlling heat transmission in nanostructures is relatively difficult. Modern technologies, including the creation of quantum dots, superlattices, and interface modification might be used as potential methods to engineer heat transport at the nanoscale, where interfaces play significant roles in heat transmission. These innovations are helping us to prepare for the era of atomic chip fabrication. The thermal management of super-large scale-integrated circuits and other power semiconductor devices would also benefit greatly from additional research on interfacial heat transport in monoatomic layers and low-dimension materials.

Soft computing techniques have a significant impact on science, image recognition, pattern identification, cyber security, engineering and manufacturing, as well as technological fields. Soft computing (SC) techniques, often indicated with terms, such as machine learning (ML), neural networks, artificial intelligence (AI) and computer vision are increasingly being adopted to solve complex problems, due to the lower cost of computation and their higher flexibility and accuracy in comparison with physical-based numerical models. Soft computing models are capable of recognizing meaningful patterns in complex problems and often adopt nature-inspired techniques using evolutionary algorithms. Strong evidence from studies indicates how standalone models in soft computing can overcome the common limitations of other predictive models in different fields.

This Special Issue of Mathematics, entitled “Recent Computational Analysis and Simulation for Mathematical Soft computing modelling and Nanoscience: Optimisation techniques”, welcomes the latest studies that focus on materials design and validations; learning techniques, soft computing models, fabrication, characterization, and theoretical analyses; devices and their applications; thermal measurement technologies, etc.

Prof. Dr. Chakravarthula S. K. Raju
Dr. Puneet Rana
Prof. Dr. Nehad Ali Shah
Prof. Dr. B.C. Prasannakumara
Dr. Mamatha S. Upadhya
Guest Editors

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• soft computing techniques
• mesoscopic/macroscopic formulations
• physical nanomaterial models for experimental design and characterization
• heat enhancement
• nanoparticles
• numerical simulations
• thermal transferring in nanostructures
• thermal measurement technologies
• heat transfer
• multi-scale computational approaches
• artificial intelligence
• machine learning
• image processing
• entropy analysis
• hybrid models
• combination of mathematical algorithms
• discretization methods
• particle physics