Deep Learning for Efficient Modeling of High Dimensional Spatiotemporal Physics

Speaker: Dr. Arvind T. Mohan , Los Alamos National Laboratory

Date: Monday, February 24, 2020

Time: 4:00 PM to 5:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: Seminar Room D463 (Star)

Event Type: Seminar

Room Description:

Host: Chris Rackauckas, MIT Mathematics

Contact: Valentin Churavy, vchuravy@csail.mit.edu

Relevant URL:

Speaker URL: None

Speaker Photo:
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Reminders to: csail-related@csail.mit.edu

Reminder Subject: TALK: Deep Learning for Efficient Modeling of High Dimensional Spatiotemporal Physics

Turbulence is an exceptionally complex and high-dimensional phenomena, exhibiting spatio-temporal dynamics, non-linearity and chaos. In an era where vast quantities of such DNS data are generated; building practical, physics-driven reduced order models (ROM) of such phenomena are crucial. While Deep neural networks for spatio-temporal data have shown considerable promise, they face severe computational bottlenecks in learning extremely high dimensional datasets, often with > 10^9 degrees of freedom. These application-agnostic networks may also lack physical constraints and interpretability that is desired in scientific ROMs. In this work, we present our efforts in integrating the strong mathematical and physical foundations underlying numerical methods and wavelet theory with deep neural networks. In this talk, we demonstrate computationally efficient learning of 3D turbulence with embedded physics constraints for improved interpretability and physics guarantees, and outline ongoing efforts.

Bio
Dr. Arvind Mohan is a Postdoctoral researcher in the Center for Nonlinear Studies and the Computational Physics and Methods group at Los Alamos National Laboratory. He obtained his PhD in Aeronautical and Astronautical Engineering from The Ohio State University with research in Computational Fluid Dynamics, data-driven aerodynamics and stall failure for aircraft wings. His current research is focused on embedding domain knowledge and physics for deep learning algorithms in turbulence and fluid mechanics, with high performance computing. His other research efforts are deep learning collaborations at Los Alamos in nuclear physics, earth sciences and astrophysics. Dr. Mohan has also organized two international conferences in machine learning and physics at LANL, with hundreds of attendees from all over the globe.

Research Areas:
AI & Machine Learning

Impact Areas:
Big Data

This event is not part of a series.

Created by Valentin Churavy Email at Monday, February 17, 2020 at 3:06 PM.