AM Seminar: The Evolving Landscape of AI for Science and Engineering: Bridging Simulation, Experiment, and Multi-scale Dynamics

Presenter: Aditi Krishnapriyan, Assistant Professor, UC Berkeley

Description: Recent advances in large-scale scientific datasets are creating new opportunities for machine learning (ML) methods to more effectively capture scientific phenomena with greater accuracy and reach. In this talk, I will discuss how these advances are both shifting ML design paradigms and enabling new scientific inquiries. This includes investigations into understanding if neural networks can autonomously discover fundamental physical relationships from data, and demonstrating how more flexible machine learning modeling design choices enable capturing physical dynamics across multiple scales. I will also explore how generative modeling approaches rooted in statistical physics can be applied to accelerate the sampling of dynamic pathways, and as a framework to align and bridge the gap between simulated data and experimental observations.

Bio: Aditi Krishnapriyan is an Assistant Professor at UC Berkeley where she is part of Chemical and Biomolecular Engineering, Electrical Engineering and Computer Sciences, and Berkeley AI Research; as well as a faculty scientist in the Applied Mathematics division at Lawrence Berkeley National Laboratory. She holds a PhD from Stanford University, supported by the DOE Computational Science Graduate Fellowship, was the Luis W. Alvarez Fellow in Computing Sciences at Lawrence Berkeley National Laboratory, and is a recipient of the Department of Energy Early Career Award and RCSA Scialog. Her research focuses on developing physics-inspired machine learning methods that bridge machine learning with physical science applications to capture phenomena across multiple length and timescales.

Hosted by: Applied Mathematics