Dr. Vikas Kumar Jayswal is an Assistant Professor of Mathematics in School of Computer Science & Engineering at IILM University, Gurugram. He is an academician and researcher with four years of experience in teaching and research his primary research lie on High-order shock-capturing numerical schemes and their applications using Deep Learning, particularly Weighted Essentially Non-Oscillatory (WENO) and Essentially Non-Oscillatory (ENO) methods integrated with neural networks for solving classification and regression problems using frameworks such as TensorFlow, Keras, and PyTorch.

In addition to teaching courses like Engineering Mathematics, Numerical Analysis, Linear Algebra, Differential Equations, Multivariable Calculus, Transforms Techniques he possesses technical proficiency in Python, MATLAB, Octave, Wolfram Mathematica and LaTeX.

Educational Qualifications:

• Ph.D. in Applied Mathematics from SRM Institute of Science and Technology,Kattankulathur, Chennai.
• M.Sc. in Mathematics from Indian Institute of Technology Madras (IIT-M), Chennai.
• B.Sc. in Mathematics from Dr. Ram Manohar Lohia Avadh University, Faizabad

Publications Record:

•  ENO classification and regression neural networks for numerical approximation of discontinuous flow problems, Soft Computing. Vol 28, iss 20, pp 11787-117982024,2024. DOI: https:10.1007/s00500-024-09944-0
• On the learning of high order polynomial reconstructions for essentially non-oscillatory schemes, Physica Scripta. Vol 99, issue 11,pp 116009, 2024. DOI:10.1088/1402-4896/ad7f97
• Smooth Solution Region Based Enhanced Hybrid Third-Order WENO Schemes, Computational and Applied Mathematics, vol 44,142(2025). DOI: http://doi.org/10.1007/s40314-025-03093-2
• A new global smoothness indicator of fifth order non-oscillatory scheme for hyperbolic conservation laws, The Journal of Analysis.2024. DOI: 10.1007/s41478-024-00739-y

Book Chapter/Conference paper: 

Learning Numerical Viscosity Using Artificial Neural Regression Network. Computational Sciences-Modelling, Computing and Soft Computing, DOI: https://doi.org/10.1007/978-981-16-4772-7_3