Research Interests
I grew up looking at things which were behaving in a certain way, some of which I could explain and some of which I couldn't at that time. The question, Why, was like a splinter in my mind, didn't allowed to sleep peacefully until I explored the reason behind.
During the school time, I was introduced to various subjects, but I liked the physics. Later I realized, the 'physics' is a very general term for every phenomena.
It was the first time, when I got the real essence of Mechanics and Dynamics. Since then I am interested in mechanics and dynamics. With the advent of journey to learn more, I learned various tools and techniques to explain and understand the phenomena. It was the time when I realized the importance of symbols, notations, theorems, etc of Mathematics, which earlier seemed to make no sense at all. Slowly understanding mathematics and its use became extremely important and consistent. From linear algebra to solving ODEs and PDEs to stochastic process covering statistics and probability, I gathered various arrows in my arsenal to understand physical processes and to solve problems.
In both its theoretical and applied incarnations. I got involved in understanding the underlying physics and simulating the problems of planetary science/geophysics, contact mechanics, and a few other random mechanics problems. Later I studied Optimization. I must say, a very fascinating term in itself, and it starts to fascinate even more when I realized that in almost every phenomena, optimization is either directly or indirectly involved, either its Lagrange equation of motion, contact mechanics, Fermat's principle of light or Principle of least action. During this course of time only, I came across a term "BioMimicry", a mind-boggling concept which changed my way of looking at nature. It included biologically inspired algorithms, GA, PSO, ACO, etc and later ANN, the thought always fascinated me that the little understanding of the natural processes have helped us so much in solving problems, what if we can understand more complex phenomena of the nature and how everything is in such a delicate balance and in harmony with each other.
Mathematical modeling of physical phenomena is a broad field, but the true difference comes when the task which would seem to work upon physically can be modeled to a great accuracy.
It can be very intriguing in terms of complexity and making it as close to a real life phenomena.
During my thesis, I worked in contact mechanics, basically mechanics and dynamics along with optimization, where I simulated the tire mechanics under the light of contact mechanics.
Simulation, as we know, is the imitation of the operation of a real-world process/system over time. It helped me to gain insight into the functioning of scientific modelling. The idea that made me inclined for this term is that Simulation can be used to understand the underlying physics and courses of action even when the real system cannot be engaged, because it may not be accessible, or it may be dangerous or unacceptable to engage, or it is being designed but not yet built, or it may simply not exist.
With the new technologies emerging, the machine learning(don't get confused with AI) is making it mark in almost all the fields. Earlier task which appeared to be extremely difficult can now be solved by augmenting machine learning in the modeling. Neural networks and other algorithm can help in modeling the physical phenomena to a great accuracy.
