Dr Kritesh Gupta

About

My research interests and expertise broadly lie in the domain of nanoscale mechanics and computational characterization of the material system on different scales. The research findings originating from our investigations are regularly published in reputed journals (with publishers like RSC, IOP, Elsevier, Wiley, Springer, and Techno Press), conferences, and books.

The present focus of my research is on multi-principle element alloys (MPEAs) and their nanoscale investigation which encompasses different critical aspects related to solid mechanics, materials, and structures. Please refer to different sections of this website for further details.

Employment

• Amrita Vishwa Vidyapeetham University Assistant Professor
  2023 - Present
• Padmasri Dr BV Raju Institute of Technology Assistant Professor
  2023 - 2023
• National Institute of Technology Silchar Research Scholar
  2019 - 2022

Education

• National Institute Of Technology Silchar PhD
  2019 - 2023
• National Institute of Technology Silchar Masters of Technology (CAD CAM & Automation)
  2017 - 2019
• Institution of Engineers AMIE
  2012 - 2016

Projects & Funding

Projects & funding information is unavailable.

Publications (42)

• Deciphering the Intricacies of Pancreatic Malignancy Through Advanced Deep Learning Methodologies
  2026 DOI: 10.1007/978-3-031-98356-6_15
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• Sustainable design of solar chimney power plants: A hybrid neural network approach for thermo-economic optimization
  Renewable Energy 2026 DOI: 10.1016/j.renene.2025.124154
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• Explainable machine learning for comprehensive characterization of poly (6-(Ethoxybenzothiazole acrylamide)) resin for removal of Th(IV), As(V), and Hg(II) ions from aqueous solution
  Environmental Geochemistry and Health 2025 DOI: 10.1007/s10653-025-02727-y
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• Optimization of hybrid solar chimney power plants (HSCPPs): A review of multi-objective approaches
  Applied Energy 2025 DOI: 10.1016/j.apenergy.2025.126214
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• A novel comparative study of machine learning surrogate models for solar chimney (SC) plant performance evaluation: Thermo-physical insights
  Energy 2025 DOI: 10.1016/j.energy.2025.136744
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• Multi-criteria optimization of nanofluid-based solar collector for enhanced performance: An explainable machine learning-driven approach
  Energy 2025 DOI: 10.1016/j.energy.2025.135212
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• Machine Learning in Nanoscale Materials Design
  Springer Nature Singapore 2025 DOI: 10.1007/978-981-95-2660-4
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• Strategic flood mitigation: leveraging machine learning for dynamic flow abstraction and release
  International Journal of Disaster Resilience in the Built Environment 2025 DOI: 10.1108/ijdrbe-02-2025-0020
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• Development of Data-Driven Predictive Framework for Nanofluid-based Solar Thermal Collector: A Machine Learning Approach
  E3S Web of Conferences 2024 DOI: 10.1051/E3SCONF/202455902003
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• Probing the Genetic Algorithm Assisted Vigenere Cipher Cryptanalysis
  2024 DOI: 10.1109/AMATHE61652.2024.10582196
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