Dr. Vikas Kumar Jayswal is an Assistant Professor in the Department of Computer Science with Applied Mathematics and Computing at IILM University, Gurugram. His research focuses on the intersection of numerical analysis, scientific machine learning, and artificial intelligence, with particular emphasis on Weighted Essentially Non-Oscillatory (WENO) and Essentially Non-Oscillatory (ENO) schemes for solving hyperbolic conservation laws. He is actively engaged in developing AI-driven numerical methods and exploring the application of deep learning and Physics-Informed Neural Networks (PINNs) for computational science and engineering.

Dr. Jayswal has published research papers in reputed international journals, including Soft Computing, Physica Scripta, The Journal of Analysis, and Computational and Applied Mathematics. His research contributes to advancing computational mathematics by integrating traditional numerical techniques with modern machine learning approaches.

As an educator, he teaches courses in Applied Mathematics, Applied Calculus, Applied Linear Algebra, Discrete Mathematics, emphasizing problem-solving, computational thinking, and hands-on learning. He is committed to fostering interdisciplinary research and preparing students with the mathematical and computational skills required to address real-world challenges in computer science and engineering.

Educational Qualification

• Ph.D., Applied Mathematics, SRM Institute of Science and Technology, Chennai, Tamil Nadu (2025)
  – Topic: High Order Non-Oscillatory Reconstruction and Deep Learning Approach for Numerical Solutions of Hyperbolic Conservation Laws (Full Time)
• M.Sc., Mathematics, Indian Institute of Technology Madras (IIT Madras), Chennai (2014)
• B.Sc., Mathematics, Dr. Ram Manohar Lohia Avadh University, Faizabad, Uttar Pradesh (2010)

Research and Scholarly Publication

PUBLICATIONS (SCOPUS/SCIE)

• “ENO Classification and Regression Neural Networks for Numerical Approximation of Discontinuous Flow Problems”, Vikas Kumar Jayswal, Prashant Kumar Pandey, Ritesh Kumar Dubey, Soft Computing, Vol 28, Iss 20, pp 11787-11798, 2024. DOI:10.1007/s00500-024-09944-0.
• “On the Learning of High Order Polynomial Reconstructions for Essentially Non-Oscillatory Schemes”, Vikas Kumar Jayswal, Ritesh Kumar Dubey, Physica Scripta, Vol 99, Iss 11, pp 116009, 2024. DOI:10.1088/1402-4896/ad7f97.
• “A New Global Smoothness Indicator of Fifth Order Non-Oscillatory Scheme for Hyperbolic Conservation Laws”, Vikas Kumar Jayswal, Ritesh Kumar Dubey, The Journal of Analysis, 2024. DOI: 10.1007/s41478-024-00739-y.
• “Smooth Solution Region Based Enhanced Hybrid Third-Order WENO Schemes”, Vikas Kumar Jayswal, Sabana Parvin, Ritesh Kumar Dubey, Computational and Applied Mathematics, Vol 44, 142 (2025). DOI: http://doi.org/10.1007/s40314-025-03093-2.

BOOK CHAPTER

• “Learning Numerical Viscosity Using Artificial Neural Regression Network”, Ritesh Dubey, Anupam Gupta, Vikas Kumar Jayswal, Prashant Pandey, DOI-10.1007/978-981-16-4772-7_3.