Nitu Kumari
Nitu Kumari

My research lies at the interface of Mathematical Modelling, Differential Equations, Scientific Machine Learning, Nonlinear Analysis, and Data-Driven Computation. For more than two decades of my research journey, a central question has been: How can we model real-world phenomena using mathematics and data to solve real-world problems? To achieve this, we must connect the concepts of Mathematical Biology with Deep Learning and Machine Learning-based techniques to build models and then study them.

In pursuing this objective, I have developed models of linguistic evolution in India and challenged the Census data; models of epidemics; and models of ecological evolution using Ordinary and Partial Differential Equations. I have also connected these models with real-world data through parameter estimation methods.

Over the last four years, I have become interested in formulating ecological models from scratch using real-world data. For this purpose, I use Machine Learning-based algorithms.

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Research areas

Recent publications

  1. Rana, Kanav Singh, and Nitu Kumari. Dynamic mode decomposition with multiple initial conditions for linearizing nonlinear partial differential equations. Engineering with Computers (Springer) 42, no. 5 (2026): 156.
  2. Nitu Kumari, Anurag Singh and Arun Kumar (2026) The first mathematical model for elk–wolf interaction in Yellowstone National Park using the E-SINDy algorithm, Ecological Modelling (Elsevier) 516, 111574.
  3. Sumit Kumar and Nitu Kumari (2026). Dynamic analysis of COVID-19 transmission in India: A compartmental model with phase specific sensitivity and uncertainty evaluation. Nonlinear Science, Elsevier Vol 7, 100142.
  4. Vikas Kumar and Nitu Kumari (2026). Exploring the impact of time delay with fractional derivative and diffusion in a three species food chain model. Iranian Journal of Science, Volume 50, pages 837–856.

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