AEM Seminar: TODAY, March 27th - Dr Teresa Portone, Sandia National Laboratories

[Molly Schmitz]

University of Minnesota

Aerospace Engineering and Mechanics
Spring 2026 Seminar Series

Friday, March 27, 2026

209 Akerman Hall

2:30pm-4:30pm

AEM Seminar: 

Beyond parametric uncertainty: methods for model-form uncertainty quantification

Abstract: 

Uncertainty quantification (UQ) is critical for informing decisions because it provides a measure of confidence in model predictions, given the uncertainties present in the model. While approaches to characterize uncertainties in model parameters are well established, it is less clear how to address uncertainties arising when the equations of a mathematical model are themselves uncertain—that is, when there is model-form uncertainty (MFU). MFU often arises in models of complex physical phenomena where (1) simplifications for computational tractability or (2) lack of knowledge leads to unknowns for which appropriate mathematical forms are undefined or may not exist. Left uncharacterized, MFU can lead to errors in the governing equations (model-form error) and inconsistencies between model outputs and experimental data (model discrepancy). In this talk, I introduce several approaches that have been developed to address MFU. I then present recent work developing a hybrid physics-data MFU representation of a reduced gas-surface chemistry model for hypersonic ablation. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. 

Bio: 

Teresa Portone is Principal Member of the Technical Staff at Sandia National Laboratories in Albuquerque, NM. She holds a Ph.D. in Computational Science, Engineering, and Mathematics from the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin and a B.S. in Mathematics from the University of Alabama. She joined Sandia as a staff member in 2020. Her research focuses on developing and deploying methods to quantify uncertainty in computational models used for national security applications, with a particular focus on methods to quantify model-form uncertainty and model prediction reliability.

*Refreshments to follow in 209 Akerman Hall   

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Molly Schmitz (She/Her/Hers)

Principal Accountant, Purchasing & Payroll Specialist

Department of Aerospace Engineering & Mechanics, University of Minnesota - Twin Cities