Title: Predicting the long-term behavior of chaotic dynamical systems with scientific machine learning
Abstract
Most machine learning algorithms for time-series forecasting leverage mean-squared-error-based loss functions. In systems exhibiting deterministic chaos, such a loss function is insufficient for learning long-term invariant behavior. In this talk, we will discuss modifications to the standard time-series learning paradigm to enable the prediction of chaotic dynamical systems both in terms of short-term deterministic metrics and long-term invariant measures. Such modifications include structural changes to the architecture of the function approximation (for instance, through novel types of neural networks) as well as augmentations to the optimization algorithms. Examples for the improved performance of the proposed techniques will range from canonical systems such as the Kuramoto-Sivashinsky equations to challenging engineering and geophysics problems.
Speaker Bio
Romit Maulik is an Assistant Professor of Data Science in the College of Information Sciences and Technology at Pennsylvania State University and a Joint Appointment Faculty at the Mathematics and Computer Sciences Division at Argonne National Laboratory (Argonne). He obtained his PhD in Mechanical and Aerospace Engineering at Oklahoma State University in 2019. He was the Margaret Butler Fellow and then a Staff Scientist at Argonne National Laboratory before joining Penn State in 2023. His research centers around machine learning for scientific computing with an emphasis on scalable, physically consistent, and robust algorithms for simulation-based scientific discovery of multiscale physics from multifidelity data. His research has been supported by various Federal agencies, including the DOE, NSF, NASA, DOD, and the Kaufman Foundation. He is an Early Career Awardee of the Army Research Office.
