← Back to Event List

Applied Math Colloquium: Michael Shields (JHU)



Date & Time

October 13, 2023, 11:00 am12:00 pm


Title: UQ for ML and ML for UQ: Why Uncertainty Quantification and Machine Learning Go Hand-in-Hand

Abstract: Uncertainty Quantification (UQ) and Machine Learning (ML) play an increasingly important role in physics-based computational modeling. Especially with the recent rise of scientific machine learning (SciML) and physics-informed ML, new computational tools are being harnessed to solve bigger and more challenging problems. Moreover, UQ has become an integral part of any physics-based modeling effort because our models, as carefully developed as they may be, are rife with uncertainties (both epistemic and aleatory) in their parameters, inputs/excitations, and sometimes in the form of the models themselves. When SciML methods are then applied in these applications, additional uncertainties are introduced. In this talk, I will broadly introduce the interrelated roles that UQ and ML play in physics-based modeling. I specifically distinguish between “UQ for ML” and “ML for UQ” and describe the important role that each plays in the modern physics-based computational modeling paradigm – demonstrating the role of UQ/ML in various applications of interest ranging from multi-scale materials modeling to high energy-density physics.

We will have the Departmental Coffee and Tea from 10 to 10:45 in M&P 422.