Romit Maulik

Lecture title:

"Turbulence Modeling for Large-eddy Simulations Using Machine Learning"


In this talk, I will go through past and present efforts to leverage scientific machine learning for building improved large eddy simulation (LES) closures. I will pay particular attention to two approaches to constructing these models: 1) Using direct numerical simulation data that is used to fit a subgrid stress tensor and 2) using an online-learning paradigm where a data-driven turbulence closure is trained within the coarse-grained LES through the use of a differentiable physics simulator. In addition to comparing and contrasting these two approaches, I will also provide some novel results, from the perspective of software engineering, that connect these algorithmic contributions to real-world turbulence modeling applications.


Romit Maulik is an Assistant Professor of Information Science and Technology with an appointment in the Institute for Computational and Data Sciences at the Pennsylvania State University. In addition, he is also a Joint-Appointment Faculty at the Mathematics and Computer Sciences Division at Argonne National Laboratory. His research group, the Interdisciplinary Scientific Computing Laboratory, is primarily focused on solving large-scale computational science problems using data-intensive scientific machine learning.


Romit Maulik, Penn State, US