ACMD Seminar: Elastic Functional Bayesian Model Calibration

J. Derek Tucker
Sandia National Laboratories & Adjunct Faculty - University of Illinois Urbana-Champaign

Tuesday, July 15, 2025, 3:00-4:00 PM ET (1:00-2:00 PM MT)

In-person at Boulder 1-4020 with VTC to Bldg. 101 LR-D*
Online at: Zoom Gov (email seminar chairs for link to talk)

Abstract: Functional data are ubiquitous in scientific modeling.  For instance, quantities of interest are modeled as functions of time, space, energy, density, etc.  Uncertainty quantification methods for computer models with functional response have resulted in tools for emulation, sensitivity analysis, and calibration that are widely used.  However, many of these tools underperform when the model's parameters control not only the amplitude variation of the functional output, but also its alignment (or phase variation). We present a simple framework for Bayesian model calibration when the model responses are misaligned functional data.  Our goal is to use a metric with appropriate invariance properties, to form objective functions for alignment and to develop statistical models involving functional data. While these elastic metrics are complicated in general, we have developed a family of square-root transformations that map these metrics into simpler Euclidean metrics, thus enabling more standard statistical procedures. From this metric we develop a Bayesian model calibration framework for mis-aligned functional data. We demonstrate the technique to emulate and calibrate a physical model that correspond to platinum experiments executed using Sandia's Z-machine.

Bio: J. Derek Tucker is a Distinguished Member of the Technical Staff at Sandia National Laboratories and Adjunct Faculty at University of Illinois Urbana-Champaign. He received his B.S. in Electrical Engineering Cum Laude and M.S. in Electrical Engineering from Colorado State University in 2007 and 2009, respectively. In 2014 he received the Ph.D. degree in Statistics from Florida State University in Tallahassee, FL under the co-advisement of Dr. Anuj Srivastava and Dr. Wei Wu. He currently is leading research projects in the area of satellite image registration and statistical functional modeling of optical emissions. His research is focused on pattern theoretic approaches to problems in image analysis, computer vision, and signal processing. In 2017, he received the Director of National Intelligence Team Award for his contributions to the Signal Location in Complex Environments (SLiCE) team.

Host: Zach Grey

Note: This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)

*Safety Precaution: The hallway leading from the Courtyard to the exit closest to B-111 and B-113 will be used by contractors to move debris, machinery, and other supplies, as well as will be heavily trafficked by the contractors throughout the process. Be aware of the safety precautions posted during this time.

Note: Visitors from outside NIST must contact Meliza Lane at least 24 hours in advance.