
October 29th, 2024
2:00 pm – 5:00 pm CT
This is an online only event
Online via Zoom:
Meeting ID: 91095173841
Passcode: 782513
Instructors: Romit Maulik and members of the Interdisciplinary Scientific Computing Laboratory
Affiliation: Pennsylvania State University and Argonne National Laboratory
Host: Suparno Bhattacharyya, Assistant Research Scientist, TAMIDS
Abstract: Advances in high-performance computing (HPC) have empowered us to perform large-scale simulations for billions of variables in coupled fluid-structure systems involving complex geometries and multiphase flows. High-fidelity simulations via coupled nonlinear partial differential equations (PDE) have been providing invaluable physical insight for the development of new designs and devices in engineering applications. Despite efficient numerical methods and powerful supercomputers, state-of-the-art computational mechanics simulations still lead to prohibitive costs for many-query problems such as optimization and control.
Scientific Machine Learning (SciML) has emerged as a powerful paradigm for solving complex scientific and engineering problems in relatively cheaper costs. By leveraging the capabilities of deep neural networks, SML offers innovative approaches to modeling, simulation, and optimization. This workshop will delve into the cutting-edge techniques of Physics-Informed Neural Networks (PINNs), Neural Ordinary Differential Equations (Neural ODEs), Neural Operators, and Differentiable Physics Algorithms, highlighting their recent advancements and applications.
These algorithms offer unique advantages for addressing specific scientific and engineering challenges. PINNs combine physical laws with neural networks to solve PDEs, while Neural ODEs model dynamical systems using continuous-time neural networks. Neural Operators extend neural networks to operate on functions or fields, enabling efficient modeling of complex, high-dimensional data. Differentiable Algorithms integrate machine learning techniques into traditional algorithms, allowing for end-to-end optimization and learning. This workshop will provide participants with a solid foundation in SciML and the latest developments in these techniques, equipping them to address a wide range of scientific and engineering challenges and contribute to the forefront of SciML research.
Biography: Romit Maulik is an Assistant Professor in the College of Information Sciences and Technology at Pennsylvania State University (Penn State). He is also a co-hire in the Institute for Computational and Data Sciences at Penn State and a Joint Appointment Faculty at Argonne National Laboratory. He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University (in 2019) and was the Margaret Butler Postdoctoral Fellow (from 2019-2021) before becoming an Assistant Computational Scientist at Argonne National Laboratory (from 2021-2023). His research has been funded by the DOE, NSF, ARO, Argonne and Los Alamos National Laboratories and he is also a member of RAPIDS2, a DOE SCIDAC Institute for Artificial Intelligence. His group studies high-performance multifidelity scientific machine learning algorithm development with applications to various multiphysical nonlinear dynamical systems such as those that arise in fluid dynamics, weather and climate modeling, nuclear fusion, and beyond. He is an Early Career Awardee from the Army Research Office.