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Dr. Kritesh Kumar Gupta

Assistant Professor, School of Artificial Intelligence, Center for Computational Engineering and Networking (CEN), Amrita Vishwa Vidyapeetham, Coimbatore

Qualification: Ph.D
g_kriteshkumar@cb.amrita.edu
ORCID iD
Google Scholar Profile
Scopus Author ID
Research Interest: Materials-informatics, Digital Twin, ML/ DL/ CV, Physics-informed ML model, Atomistic simulations, Computational Materials Science, Impact and Shock Mechanics, Nano-materials, Data-driven materials discovery, Digital twin, Artificial intelligence, Multiscale material modelling & analysis, 2d materials and heterostructures, Multi-scale mechanics, Nano-mechanics, Stochastic analysis

Bio Publications Qualification Experience Teaching  Reviewers
Bio

Dr. Kritesh Kumar Gupta is currently working as Assistant Professor in School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore. He completed his doctoral studies from National Institute of Technology Silchar, India specializing in Physics informed Machine Learning models for nanoscale mechanics and computational materials science. Dr. Gupta is actively engaged in collaborative research with renowned national and international institutions, including IIT Kanpur, IIT Bombay, Swansea University (U.K.), and University of Southampton (U.K.). To this end, he has extensively employed atomistic simulations and computationally efficient machine learning algorithms to gain insights into the intricate mechanics of materials system on different length scale. His profound expertise lies in utilizing advanced machine learning and deep learning techniques to perform seamless and efficient materials discovery. So far, he has published several high-impact factor papers in well-respected scientific journals and presented his research on several national and international platforms. His current research interests revolve around the development of digital twins for designing the function-specific material systems. By harnessing the power of advanced computational techniques and AI, he aims to create virtual replicas that accurately represent the behavior and properties of real-world materials, enabling efficient and innovative design approaches.

Positions

  • Current Positions 
    Assistant Professor,  Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham Coimbatore ,From: July 2023-till date
  • Past Positions 
    Assistant Professor,  Department of Mechanical Engineering, B.V. Raju Institute of Technology Narsapur, Telangana ,From: April 2023 – July 2023
Publications

Journal Article

Year : 2024

Probing atomistic deformation behavior of graphene-coated Al0.3CoCrFeNi high-entropy alloy under nanoindentation

Cite this Research Publication : Barman, S., Gupta, K.K. and Dey, S., 2024. Probing atomistic deformation behaviour of graphene-coated Al0. 3CoCrFeNi High-Entropy Alloy under nanoindentation. Journal of Micromechanics and Molecular Physics. https://doi.org/10.1142/S2424913024500036

Publisher : World Scientific

Year : 2024

Random topological defects in double-walled carbon nanotubes: On characterization and programmable defect-engineering of spatio-mechanical properties

Cite this Research Publication : Roy, A., Gupta, K.K., Dey, S. and Mukhopadhyay, T., 2024. Random topological defects in double-walled carbon nanotubes: On characterization and programmable defect-engineering of spatio-mechanical properties. Advances in Nano Research, 16(1), p.91. DOI: 10.12989/anr.2024.16.1.091

Publisher : Techno Press

Year : 2023

Stochastic Performance of Journal Bearing With Two-Layered Porous Bush—A Machine Learning Approach

Cite this Research Publication : Barman, S., Gupta, K.K., Kushari, S. and Dey, S., 2023. Stochastic Performance of Journal Bearing With Two-Layered Porous Bush—A Machine Learning Approach. Journal of Tribology, 145(10), p.104101. https://doi.org/10.1115/1.4062487

Publisher : ASME

Year : 2023

Probabilistic investigation of geometric responses in Wire EDM machined complex-shaped profile: A machine learning based approach

Cite this Research Publication : Saha, S., Kumar Gupta, K., Ranjan Maity, S. and Dey, S., 2023. Probabilistic investigation of geometric responses in Wire EDM machined complex-shaped profile: A machine learning based approach. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 237(12), pp.1798-1809.

Publisher : Sage Journals Home

Year : 2023

Probing the molecular-level energy absorption mechanism and strategic sequencing of graphene/Al composite laminates under high-velocity ballistic impact of nano-projectiles

Cite this Research Publication : Gupta, K.K., Mukhopadhyay, T., and Dey, S., "Probing the molecular-level energy absorption mechanism and strategic sequencing of graphene/Al composite laminates under high-velocity ballistic impact of nano-projectiles", Applied Surface Science, Volume 629, 30 August 2023, 156502, SCI, IF: 7.392, 2023.

Publisher : Elsevier

Year : 2022

Probing the stochastic unconfined compressive strength of lime-RHA mix treated clayey soil

Cite this Research Publication : Gautam, Gupta, K.K., Bhowmik, D. and Dey, S., "Probing the stochastic unconfined compressive strength of lime-RHA mix treated clayey soil," ASCE’s Journal of Materials in Civil Engineering, Volume 35, Issue 3 https://doi.org/10.1061/(ASCE)MT.1943-5533.0004638, SCIE, IF: 3.266, 2022.

Year : 2022

Stochastic performance of journal bearing with two-layered porous bush- A machine learning approach

Cite this Research Publication : Barman, S., Gupta, K.K., Kushari, S. and Dey, S., "Stochastic performance of journal bearing with two-layered porous bush- A machine learning approach," Journal of Tribology, Oct 2023, 145(10): 104101, SCIE, IF: 1.891

Year : 2022

A Gaussian process regression based probabilistic analysis of geometry-based responses in WEDM of a complex-shaped profile

Cite this Research Publication : Saha, S., Gupta, K.K., Maity, S.R. and Dey, S., "A Gaussian process regression based probabilistic analysis of geometry-based responses in WEDM of a complex-shaped profile," Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. SCI, IF: 2.759, 2022.

Year : 2022

Meta-modeling assisted probabilistic first ply failure analysis of laminated composite plates – RS-HDMR and GPR based approach

Cite this Research Publication : Kushari S., Gupta, K. K., Vaishali and Dey, S., "Meta-modeling assisted probabilistic first ply failure analysis of laminated composite plates - RS-HDMR and GPR based approach," Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44, Article number: 374, SCIE, IF: 2.361, 2022.

Publisher : Springer

Year : 2022

Probabilistic investigation of temperature-dependent vibrational behavior of hetero-nanotubes

Cite this Research Publication : Roy, A., Gupta, K.K. and Dey, S., "Probabilistic investigation of temperature-dependent vibrational behaviour of hetero-nanotubes," Applied Nanoscience, 12, pages 2077–2089, SCI, IF: 3.869, 2022.

Publisher : Springer

Year : 2022

Hybrid machine-learning-assisted stochastic nano-indentation behaviour of twisted bilayer graphene

Cite this Research Publication : Gupta, K.K., Roy, L. and Dey, S., "Hybrid machine-learning-assisted stochastic nano-indentation behaviour of twisted bilayer graphene," Journal of Physics and Chemistry of Solids, Volume 167, August 2022, 110711, SCIE, IF: 4.383, 2022.

Publisher : Elsevier

Year : 2022

Probing the stochastic fracture behavior of twisted bilayer graphene: Efficient ANN based molecular dynamics simulations for complete probabilistic characterization

Cite this Research Publication : Gupta, K.K., Roy, A., Mukhopadhyay, T., Roy, L., and Dey, S., "Probing the stochastic fracture behavior of twisted bilayer graphene: Efficient ANN based molecular dynamics simulations for complete probabilistic characterization," Materials Today Communications, Volume 32, August 2022, 103932, SCIE, IF: 3.662, 2022.

Publisher : Elsevier

Year : 2022

Hybrid machine-learning-assisted quantification of the compound internal and external uncertainties of graphene: towards inclusive analysis and design

Cite this Research Publication : Gupta, K.K., Mukhopadhyay, T., Roy, L. and Dey, S., "Hybrid machine-learning-assisted quantification of the compound internal and external uncertainties of graphene: towards inclusive analysis and design," Materials Advances, Issue 2, 2022, SCIE, IF: 5.0

Publisher : Materials Advances

Year : 2022

High-velocity ballistics of twisted bilayer graphene under stochastic disorder

Cite this Research Publication : Gupta, K.K., Mukhopadhyay, T., Roy, L. and Dey, S., (2022), "High-velocity ballistics of twisted bilayer graphene under stochastic disorder," Advances in Nano Research, Volume 12, Number 5, May 2022, pages 529-547, DOI: https://doi.org/10.12989/anr.2022.12.5.529

Publisher : Advances in Nano Research

Year : 2021

Data-driven probabilistic performance of Wire EDM: A machine learning based approach

Cite this Research Publication : Saha, S., Gupta, K.K., Maity, S.R. and Dey, S., "Data-driven probabilistic performance of Wire EDM: A machine learning based approach," Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Volume 236, Issue 6-7 https://doi.org/10.1177/09544054211056417, SCI, IF: 2.759, 2021.

Publisher : Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Year : 2021

Compound influence of topological defects and heteroatomic inclusions on the mechanical properties of SWCNTs

Cite this Research Publication : Roy, A., Gupta, K.K., Naskar, S., Mukhopadhyay, T. and Dey, S., "Compound influence of topological defects and heteroatomic inclusions on the mechanical properties of SWCNTs," Materials Today Communications, Volume 26, March 2021, SCIE, IF: 3.662, 2021.

Publisher : Elsevier

Year : 2021

Probing the stochastic dynamics of coronaviruses: Machine learning assisted deep computational insights with exploitable dimensions

Cite this Research Publication : Mukhopadhyay, T., Naskar, S., Gupta, K.K., Kumar, R., Dey, S. and Adhikari, S., "Probing the stochastic dynamics of coronaviruses: Machine learning assisted deep computational insights with exploitable dimensions," Advanced Theory and Simulations. SCIE, IF: 4.105, 2021. DOI: https://doi.org/10.1002/adts.202000291.

Publisher : Wiley Online Library

Year : 2021

Sparse machine learning assisted deep computational insights on the mechanical properties of graphene with intrinsic defects and doping

Cite this Research Publication : Gupta, K.K., Mukhopadhyay, T., Roy, A., Roy, L. and Dey, S., 2021. "Sparse machine learning assisted deep computational insights on the mechanical properties of graphene with intrinsic defects and doping", Journal of Physics and Chemistry of Solids, Volume 155, August 2021, 110111, SCIE, IF: 4.383

Publisher : Elsevier

Year : 2020

Probing the compound effect of spatially varying intrinsic defects and doping on mechanical properties of hybrid graphene monolayers

Cite this Research Publication : Gupta, K.K., Mukhopadhyay, T., Roy, A. and Dey, S., 2020. "Probing the compound effect of spatially varying intrinsic defects and doping on mechanical properties of hybrid graphene monolayers". Journal of Materials Science & Technology, Volume 50, 1 August 2020, Pages 44-58, SCIE, IF: 10.319

Publisher : Elsevier

Conference Paper

Year : 2022

Influence of alloying elements on mechanical deformation of AlCoCrFeNi High-Entropy Alloy

Cite this Research Publication : Kritesh Kumar Gupta, "Influence of alloying elements on mechanical deformation of AlCoCrFeNi High-Entropy Alloy," 67th congress of the Indian Society of Theoretical and Applied Mechanics (ISTAM 2022), IIT Mandi, 2022.

Year : 2020

Effect of spatial distribution of nanopores on mechanical properties of monolayer graphene

Cite this Research Publication : K Saumya, K K Gupta, A Roy, and S Dey, "Effect of spatial distribution of nanopores on mechanical properties of monolayer graphene," Second International Conference on Materials Science and Manufacturing Technology 9-10 April 2020, IOP Conference Series: Materials Science and Engineering, Volume 872, Hotel Aloft, Coimbatore, Sri Shakthi Institute of Engineering and Technology, 2020.

Publisher : IOP Conference Series: Materials Science and Engineering

Year : 2020

Effect of silicon dopant on mechanical properties of monolayer graphene

Cite this Research Publication : V K Majeti, A Roy, K K Gupta, S Dey, "Effect of silicon dopant on mechanical properties of monolayer graphene," Second International Conference on Materials Science and Manufacturing Technology 9-10 April 2020, IOP Conference Series: Materials Science and Engineering, Volume 872, Hotel Aloft, Coimbatore, organized by Sri Shakthi Institute of Engineering and Technology, 2020.

Publisher : IOP Conference Series: Materials Science and Engineering

Year : 2020

Machine Learning-Based Molecular Dynamics Simulations of Monolayered Graphene

Cite this Research Publication : Kritesh Kumar Gupta, Lintu Roy & Sudip Dey, "Machine Learning-Based Molecular Dynamics Simulations of Monolayered Graphene," International Conference on Recent Advances in Computational and Experimental Mechanics (ICRACEM 2020), Vol II pp 251–263, IIT Kharagpur, 2020.

Publisher : Springer

Year : 2019

Comparative study of various defects in monolayer graphene using molecular dynamics simulation

Cite this Research Publication : Kritesh Kumar Gupta, Aditya Roy & Sudip Dey, "Comparative study of various defects in monolayer graphene using molecular dynamics simulation," International Conference on Applied Mechanical Engineering Research (ICAMER 2019), NIT Warangal, 2019

Publisher : Springer

Year : 2019

Effect of Temperature on the Fracture Strength of Perfect and Defective MonoLayered Graphene

Cite this Research Publication : Kritesh Kumar Gupta, Sudip Dey, "Effect of Temperature on the Fracture Strength of Perfect and Defective MonoLayered Graphene" in 2nd International Conference on Computational Methods in Manufacturing (ICCMM 2019), IIT Guwahati, 2019.

Publisher : Springer

Book Chapter

Year : 2023

Uncertainty Quantification—An Eternal Future of Engineering and Technology

Cite this Research Publication : Dey, S. and Gupta, K.K., "Uncertainty Quantification—An Eternal Future of Engineering and Technology," In Engineering Pedagogy: A Collection of Articles in Honor of Prof. Amitabha Ghosh (pp. 145-155). Singapore: Springer Nature Singapore. DOI: 10.1007/978-981-19-8016-9_11, 2023. 

Publisher : Springer Nature

Year : 2022

Ballistic Performance of Bi-layer Graphene: Artificial Neural Network Based Molecular Dynamics Simulations

Cite this Research Publication : Gupta, K.K., Roy, L., Dey, S., "Ballistic Performance of Bi-layer Graphene: Artificial Neural Network Based Molecular Dynamics Simulations," In: Kushvaha, V., Sanjay, M.R., Madhushri, P., Siengchin, S. (eds) Machine Learning Applied to Composite Materials. Composites Science and Technology, Springer, Singapore. https://doi.org/10.1007/978-981-19-6278-3_7 , 2022.

Publisher : Springer, Singapore

Qualification
  • PhD (Year): 2019-2023
    Specialization: Physics informed Machine Learning models, Computational Materials Science
    Thesis title:  Mechanical properties and ballistic impact behaviour of hybrid graphene nanostructures- A machine learning approach
  • M.Tech (Year): 2017
    Specialization: Computational Materials Science
    Thesis title: Mechanical behaviour of Monolayer graphene- A Molecular Dynamics approach
Experience

July 2023 – Till date

Assistant Professor (Standard Track), Amrita School of AI, Amrita Vishwa Vidyapeetham, India

  • Currently handling the course on Discrete Mathematics
  • Research outcomes in the domain of Materials-informatics

April – June, 2023

Assistant Professor, Dept. of M.E., BVRIT Narsapur, Telangana State, India

  • Handled the M.Tech. curriculum of Finite Element Analysis
  • Handled the Computer Aided Design Lab

2019 – 2022

Teaching and Research Assistant, National Institute of Technology Silchar

  • Solely handled the curriculum of Machine Design lab of the BTech (M. E.) 3rd– year students and the curriculum of Computer Aided Design and Analysis lab (CATIA/ SolidWorks, ANSYS) of the MTech 1st – year students
  • Mentored BTech students in their final year projects, and MTech students in their thesis
  • Helped Dr. Sudip Dey (thesis supervisor) in designing and implementing a new course on Uncertainty Quantification in the UG curriculum of engineering programme in NIT Silchar, on basis of which a pedagogical article entitled “Uncertainty Quantification – An Eternal Future of Engineering and Technology” is published.
  • Thesis research in physics informed machine learning models for computational characterization of materials

2015 – 2016

Management Associate, Boomerang Business Developer Services LLP, Lucknow

  • Offshore clientele solutions
  • Providing virtual support to offshore (USA) clients
Teaching 
  • Courses taught 
    –  Finite Element Analysis, 2023, PG
    –  Discrete Mathematics, 2023, UG
    – Introduction to Materials Informatics, 2024, UG
  • Conferences/Workshops/Webinars organized (Chairman/Convener/Others) 
    – Organized two days FDP on ‘Introduction to Materials Informatics’
Reviewers

  1. Reviewers (National/International Journals; Conferences)

    International Journals 

    • Materials Today
    • Materials Today Communications
    • Physica Scripta
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