2021-01-12 - Ashesh Chattopadhyay - Deep Learning for Modeling Chaos and Geophysical Turbulence
Channel: NERSC
Date: 2021-01-20

NERSC Data Seminars Series: https://github.com/NERSC/data-seminars

Title: Deep Learning Approaches for Modeling Multi-Scale Chaos and Geophysical Turbulence

Speaker: Ashesh Chattopadhyay (Rice University)

Abstract: Our atmosphere is a coupled, chaotic, and turbulent dynamical system with multiple physical processes interacting with each other, at continuously varying spatio-temporal scales. Building efficient and accurate weather/climate models that can predict the state of the atmosphere for the near and distant future requires us to resolve a broad range of spatio-temporal scales that often take up a daunt- ing amount of computational resources. Thus, current tractable climate models often have inaccurate and crude approximations of hard-to-resolve physical processes that drastically affect our ability to predict the dynamics of the system. Here, we propose alternative data-driven approaches that utilize deep learning algorithms trained on observations or high-resolution model outputs working in conjunction with numerical models to perform carefully constructed approximations that accurately capture the physics of these hard-to-resolve processes. This can reduce computational cost while bringing more insight into poorly understood physics that can dramatically improve our ability to predict the large-scale dynamics of the atmosphere.

Bio: Ashesh Chattopadhyay did his Bachelors from the department of Mechanical Engineering at Indian Institute of Technology, Patna where he worked primarily in optimization and computational geometry. He got his masters from the University of Texas, El Paso, from the Computational Science program where his research was focused on high performance computing. Since then, he has been a PhD student at Rice University in the department of Mechanical Engineering, where he works at the intersection of theoretical deep learning, dynamical systems and turbulence modeling for broad applications in atmospheric dynamics.