Varatharaj K | Mathematics | Best Paper Award

Published on by Research Hypothesis

Best Paper Award

Varatharaj K
National Taiwan Ocean University, Taiwan

Varatharaj K
Affiliation National Taiwan Ocean University
Country Taiwan
Scopus ID 58415659900
Documents 19
Citations 148
h-index 8
Subject Area Mathematics
Event International Research Hypothesis Excellence Award
ORCID 0009-0005-6763-0773

Varatharaj K   the Best Paper Award profile recognizes the research contributions of Varatharaj K of National Taiwan Ocean University, Taiwan, with particular emphasis on mathematical modelling, nonlinear fluid dynamics, nanofluid transport, computational analysis, and intelligent numerical methods. The research record includes studies addressing heat and mass transfer, magnetohydrodynamic flows, porous media, thermal radiation, neural-network-assisted modelling, and control of complex dynamical systems. The publication record supplied for this profile includes peer-reviewed journal articles indexed through scholarly bibliographic sources and Crossref records. [1][2][3]

Abstract

Varatharaj K is a researcher in mathematics whose publication record encompasses computational mathematics and mathematical modelling of transport phenomena and nonlinear dynamical systems. The documented research includes analytical and numerical investigations of Casson and Jeffrey nanofluids, heat and mass transfer, porous media, thermal radiation, magnetohydrodynamic flow, activation energy, and computational intelligence. Recent work extends these themes toward physics-informed computational modelling and neural-network-based time-series prediction. [2][3] A further research direction concerns fractional-order adaptive and robust sliding-mode control for predefined-time synchronization of hyper-chaotic convection systems, connecting mathematical analysis with control theory and complex dynamical behaviour. [1]

Keywords

Varatharaj K; Best Paper Award; Mathematics; computational mathematics; mathematical modelling; nonlinear fluid dynamics; nanofluid flow; Casson nanofluid; Jeffrey nanofluid; heat transfer; mass transfer; porous media; magnetohydrodynamics; thermal radiation; neural-network modelling; physics-informed modelling; fractional-order control; sliding-mode control; hyper-chaotic systems; International Research Hypothesis Excellence Award.

Introduction

Mathematical modelling provides a framework for representing physical processes through differential equations, numerical methods, computational algorithms, and analytical approximations. Within this broad discipline, the mathematical study of non-Newtonian fluids and nanoscale heat-transfer systems has applications in engineering, thermal management, energy systems, and transport analysis. The publications associated with Varatharaj K address these topics through combinations of numerical computation, analytical formulation, artificial neural networks, and contemporary computational modelling approaches. [2][3]

Research Profile

The supplied bibliographic record identifies Varatharaj K as a mathematics researcher affiliated with National Taiwan Ocean University in Taiwan. The profile contains 19 indexed documents, 148 citations, and an h-index of 8. These indicators provide a bibliometric description of the supplied research record but should be interpreted in the context of database coverage, publication dates, citation accumulation, and indexing practices. [5]

  • Primary discipline: Mathematics.
  • Institutional affiliation: National Taiwan Ocean University.
  • Research orientation: Computational and applied mathematical modelling.
  • Major themes: nanofluid flow, heat and mass transfer, porous media, MHD flow, nonlinear dynamics, and computational control.
  • Computational approaches: numerical modelling, artificial neural networks, time-series modelling, and physics-informed computational methods.

Research Contributions

A notable theme in the publication record is the mathematical treatment of non-Newtonian nanofluid flow. Research on Casson nanofluids considers heat and mass transfer under conditions such as linear and nonlinear stretching surfaces and activation energy. These models are relevant to the mathematical description of coupled momentum and thermal transport problems. [4]

The research also incorporates artificial neural networks as computational tools for time-series modelling. The reported Casson–Jeffrey nanofluid study combines numerical analysis with artificial neural-network modelling, demonstrating an approach in which conventional computational solutions can be complemented by data-driven approximation. [3]

Publications

  1. Fractional-order adaptive and robust sliding mode control for predefined-time synchronization of hyper-chaotic convection systems. Journal of Computational and Applied Mathematics, 2027-02.
  2. Physics-informed computational modelling for thermal analysis of Casson nanofluid flow over linear and nonlinear stretching surfaces. International Journal of Ambient Energy, 2026-12-31.
  3. Numerical and artificial neural network time-series modeling of Casson–Jeffrey nanofluid flow over linear and nonlinear stretching surfaces in porous media. International Journal of Thermofluids, 2026-01.
  4. Linear and nonlinear stretching sheet for enhanced heat and mass transfer in Casson nanofluid with activation energy. Numerical Heat Transfer Part A: Applications, 2025.
  5. Non-linear thermal radiation and heat transfer effect on MHD flow of a micropolar fluid through a porous medium. Journal of Analysis, 2025.

Research Impact

The supplied bibliometric profile reports 148 citations and an h-index of 8 across 19 indexed documents. These measures indicate that the research outputs have accumulated citations within the indexed scholarly literature. Bibliometric indicators, however, are quantitative descriptors rather than comprehensive measures of scientific quality, originality, societal value, or methodological significance. [5]

Award Suitability

The Best Paper Award category within the International Research Hypothesis Excellence Award can be considered in relation to the documented publication record, methodological relevance, and scholarly contribution of individual research papers. For Varatharaj K, the supplied publications provide several candidates for consideration, particularly those integrating mathematical modelling with computational methods.

  • Methodological relevance: the publications apply mathematical and computational techniques to nonlinear transport and dynamical-system problems.
  • Interdisciplinary application: the research connects mathematics with fluid mechanics, heat transfer, nanofluid modelling, computational intelligence, and control theory.
  • Computational development: artificial neural networks and physics-informed modelling complement established numerical approaches. [2][3]
  • Research continuity: the supplied publications show a continuing focus on mathematical modelling of complex flow and nonlinear systems across multiple journal articles.
  • Scholarly record: the supplied profile reports 19 indexed documents, 148 citations, and an h-index of 8.

Conclusion

Varatharaj K’s documented research profile is centered on mathematics and computational modelling, with publications addressing non-Newtonian nanofluids, heat and mass transfer, porous media, magnetohydrodynamic and micropolar flows, artificial neural-network modelling, physics-informed computational analysis, and nonlinear control. The supplied record demonstrates a coherent connection between mathematical formulation and computational investigation of complex physical and dynamical systems. [1][2][3]

References

  1. Crossref. (n.d.). Fractional-order adaptive and robust sliding mode control for predefined-time synchronization of hyper-chaotic convection systems. Journal of Computational and Applied Mathematics.
    https://doi.org/10.1016/j.cam.2026.117999
  2. Crossref. (n.d.). Physics-informed computational modelling for thermal analysis of Casson nanofluid flow over linear and nonlinear stretching surfaces. International Journal of Ambient Energy.
    https://doi.org/10.1080/01430750.2026.2702413
  3. Crossref. (n.d.). Numerical and artificial neural network time-series modeling of Casson–Jeffrey nanofluid flow over linear and nonlinear stretching surfaces in porous media. International Journal of Thermofluids.
    https://doi.org/10.1016/j.ijft.2025.101534
  4. Elsevier/Scopus. (n.d.). Linear and nonlinear stretching sheet for enhanced heat and mass transfer in Casson nanofluid with activation energy. Numerical Heat Transfer Part A: Applications.
    https://doi.org/10.1080/10407782.2024.2357578
  5. Elsevier/Scopus. (n.d.). Non-linear thermal radiation and heat transfer effect on MHD flow of a micropolar fluid through a porous medium. Journal of Analysis.
    https://doi.org/10.1007/s41478-024-00777-6

Divyanee Garg | Mathematics | Research Excellence Award

Published on by Research Hypothesis

Ms. Divyanee Garg | Mathematics | Research Excellence Award

Ms. Divyanee Garg | Mathematics | Research Excellence Award | PhD Scholar | Indian Institute of Technology in Delhi | India

Ms. Divyanee Garg is an emerging researcher in quantitative finance and mathematical optimization, currently pursuing her Ph.D. in Mathematics at IIT Delhi, where she works on portfolio optimization, behavioural finance, robust allocation models, and data-driven decision techniques. Her academic journey reflects exceptional consistency, beginning with a strong foundation in Mathematics through her B.Sc. from S. S. Jain Subodh College, Jaipur, followed by an M.Sc. in Mathematics from IIT Roorkee, and culminating in her doctoral research supported by prestigious recognitions. Ms. Divyanee Garg has demonstrated outstanding academic excellence through multiple national-level achievements, including selection under the Prime Minister’s Research Fellows (PMRF) scheme and securing AIR 119 in CSIR-UGC NET (JRF), AIR 210 in GATE Mathematics, AIR 155 in JAM, and receiving the INSPIRE Scholarship from DST for five consecutive years. Professionally, she has contributed significantly as a teaching assistant in diverse mathematical domains such as Financial Mathematics, Fuzzy Sets, Optimization Methods, Econometrics, and Machine Learning, handling both undergraduate and postgraduate teaching responsibilities at IIT Delhi. Her research interests include portfolio optimization under risk measures like Expectile VaR and CVaR, cumulative prospect theory, robust optimization with neural networks, numerical optimization, and large-scale computational methods. Research skills demonstrated by Ms. Divyanee Garg include expertise in Python, R, MATLAB, LaTeX, MS Excel, and the formulation of optimization models using advanced mathematical programming techniques. She has published impactful research in reputed international journals such as Computational and Applied Mathematics and Omega, with additional manuscripts under revision. Her work has been showcased at major academic platforms, including the International Symposium at ISI Delhi, the International Conference on Computations and Data Science at IIT Roorkee, the Annual Convention of ORSI at IIT Bombay, and the EURO Conference in the UK. She has also engaged in summer schools and workshops related to large-scale optimization, strengthening her methodological foundations and collaborative experience. Her academic distinctions include district-level awards and formal recognition for academic excellence. In conclusion, Ms. Divyanee Garg exemplifies a strong blend of analytical capability, high-quality research output, and dedicated academic service, making her a promising researcher in quantitative finance and optimization. Her continuous contributions through publications, teaching, international presentations, and interdisciplinary problem-solving reflect her commitment to advancing scientific knowledge, while her growing expertise positions her for impactful leadership roles in research, innovation, and academic communities.

Profile: ORCID | Scopus | Google Scholar

Featured Publications 

  1. Garg, D., & Mehra, A. (2026). Portfolio optimization with expectile value at risk and conditional value at risk: Deviation measure and robust allocation. Computational and Applied Mathematics.

  2. Garg, D., Khan, A. Z., & Mehra, A. (2026). Enhanced indexing using cumulative prospect theory utility function with expectile risk.

  3. Garg, D., Sehgal, R., & Mehra, A. (n.d.). Data-driven approach to robust portfolio optimization using deep neural networks. Manuscript under revision.

  4. Garg, D., & Swaminathan, A. (n.d.). Numerical improvement of Gauss–Chebyshev quadrature rule. Unpublished research study.

  5. Garg, D., & Gupta, S. K. (n.d.). Optimality and duality conditions for semi-infinite programming problems. Project report.

  6. Garg, D. (n.d.). Robust allocation models using behaviour-driven portfolio optimization. Working paper.

  7. Garg, D. (n.d.). Machine learning-assisted optimisation frameworks for financial decision making. Working paper.