RIST Popular Talk -50
Nobel Prize in Physics
Date - 18th October, 2021

INVITATION FOR RIST/NEAS POPULAR TALK-50

Date and Timing: 18 Oct 2021, 04:30 pm (IST)
Join with Google Meet: https://meet.google.com/txj-auva-sfk
Nobel Prize in Physics on “Our Understanding of Complexity” and Perspectives from Data-driven Modeling of Natural and Anthropogenic Systems.

A Surjalal Sharma
University of Maryland, College Park, USA
Research Institute of Science and Technology (RIST), Imphal, India
North-East Academy of Sciences (NEAS), Imphal, India

Abstract: The Nobel Prize in Physics 2021 was awarded “for groundbreaking contributions to our understanding of complex physical systems”. The interplay between organized and irregular characteristics is a key element in the understanding of complex systems with many interdependent components and the resulting the multiscale behavior renders the modeling and prediction a challenging endeavor.

The Prize recognizes the advances in the “Physics for Climate and other complex phenomena” that provides the foundation for the development of current climate models, predictability of climate despite chaotic weather, and theory of complex systems.

The modeling of natural and anthropogenic systems in the complex systems framework is based on the dynamical systems theory and makes use of extensive data from multiple sources, from ground-based monitors to hyperspectral imagers on satellites. The data-driven approach provides a self-consistent description of multiscale behavior, enabling an integrated framework for modeling short-scale dynamics to long-term development.

Long-range correlations are inherent in complex systems and underlie the heavy-tail distributions which include extreme events. The emerging understanding of extreme events in terms of the interaction among different components, within and outside the system, is a basis for developing their modeling in the complex systems framework.

Recent applications of artificial intelligence and machine learning have provided growing evidence of new capabilities. However, machine learning in dynamic systems is in very early stages of development, mainly because of the absence of a theory of deep learning. The complex systems approach provides a framework for developing machine learning in dynamically evolving natural and anthropogenic systems.


(Prof. N. Nimai Singh)
Convener,
RIST monthly popular talk series
Head, Department of Physics
Manipur University
Canchipur – 795 003
Imphal, Manipur
Website: www.manipuruniv.ac.in