Dr. Shagun Panghal is an academician, presently working as an Assistant Professor in the School of Computer Science and Engineering at IILM University (Institute of Integrated Learning in Management), Gurugram (India). Prior to this, he worked as an Assistant Professor in the Department of Basic Sciences and Humanities at PSIT (Pranveer Singh Institute of Technology), Kanpur (India). He received his Ph.D. in Applied Mathematics from MNNIT Allahabad (Institute of National Importance), Prayagraj, Uttar Pradesh, and holds a Master’s degree in Mathematics & Scientific Computing from the same institute. During his doctoral research, he has published several research articles in prestigious refereed journals indexed in SCIE and Scopus. He has qualified CSIR NET 2016 (AIR 23), GATE 2017 (AIR 106), and IIT JAM 2015 (AIR 285), and has presented his research work at international conferences. With a strong academic background in mathematics and a keen research interest in neural network-based and physics-informed approaches to differential equations, he is committed to fostering a love for learning and empowering students with the knowledge and skills they need to excel.

Educational Qualification

• Ph.D., Applied Mathematics, MNNIT Allahabad, Prayagraj (2017 – 2023)
  – Thesis: Neural Network Algorithms for Solving Differential Equations
  – Supervisor: Dr. Pramod Kumar Yadav, Professor, Department of Mathematics
• M.Sc., Mathematics & Scientific Computing, MNNIT Allahabad, Prayagraj (2015 – 2017)
  – Percentage: 80.80% (First Division)
• B.Sc., Mathematics (Honours), Teerthanker Mahaveer University, Moradabad, Uttar Pradesh (2012 – 2015)
  – Percentage: 74.38% (First Division)

Research and Scholarly Publication

SCIE PUBLICATIONS

• Panghal S., Kumar M., “Approximate analytic solution of Burger Huxley equation using feed-forward artificial neural network”, Neural Processing Letters, 53 (2021), 2147-2163.
• Panghal S., Kumar M., “Optimization free neural network approach for solving ordinary and partial differential equations”, Engineering with Computers, 37 (2021), 2989-3002.
• Panghal S., Kumar M., “Neural network method: delay and system of delay differential equations”, Engineering with Computers (2021). https://doi.org/10.1007/s00366021-01373-z.
• Panghal S., Kumar M., “A multilayer perceptron neural network approach for the solution of hyperbolic telegraph equations”, Network: Computation in Neural Systems, 32(2-4) (2022), 65-82.

SCOPUS PUBLICATIONS

• Panghal S., Kumar M., “Multilayer Perceptron and Chebyshev Polynomials Based Neural Network for Solving Emden–Fowler Type Initial Value Problems”, International Journal of Applied and Computational Mathematics, 6, 157 (2020). https://doi.org/10.1007/s40819-020-00914-2.
• Panghal S., Kumar M., “Multilayer perceptron and Chebyshev polynomials based functional link artificial neural network for solving differential equations”, International Journal of Modeling, Simulation and Scientific Computing, 12(2), 2150011 (2021). https://doi.org/10.1142/S1793962321500112.

UNDER REVIEW / SUBMITTED

• Panghal S., Mehta P., Goel P., “Enhanced accuracy in Physics-Informed Neural Networks: A Chebyshev Polynomial-Based Approach for the Burgers’ Equation.” (Submitted).
• Panghal S., Goel P., “Backward-Compatible Chebyshev polynomials based Physics Informed Neural Networks for Stiff Phase-Field Equation.” (Under review).
• Yadav P., Panghal S., Goel P., “Mathematical modelling of drug-resistant tuberculosis in context of awareness and affordability.” (Under review).