Contributed Papers

Advancing Scientific Modeling and Decision-Making

Wed, Aug 6, 8:30 AM - 10:20 AM Room CC-214 Music City Center

Section on Statistics in Defense and National Security co: Section on Statistics in Defense and National Security

About this session

This session explores innovative approaches to scientific modeling and decision-making across diverse fields, with a focus on the integration of machine learning, uncertainty quantification, and advanced computational techniques. Presentations will highlight real-time forecasting applications, predictive models for complex systems, and data-driven solutions in material science, molecular structures, and environmental phenomena.

7 Presentations

Adaptive Conformal Prediction for Complex Inputs

8:35 AM - 8:50 AM

Co-authors: Laura Wendelberger (Lawrence Livermore National Laboratory), Laura Wendelberger (Lawrence Livermore National Laboratory)

Conformal prediction Uncertainty quantification Deep learning Computer vision

Application of DOD's VAULTIS data management framework to testing

8:50 AM - 9:05 AM

Co-authors: John Haman (IDA), John Haman (IDA)

Data Management System Testing Test Planning Department of Defense Operational Testing

WITHDRAWN Fast global-local reduced order models using neural Galerkin projections

9:05 AM - 9:20 AM

Co-authors: John Tipton (Los Alamos National Lab), Shane Coffing (Los Alamos National Lab)

PDE surrogates Hierarchical models Uncertainty quantification

Machine learning for molecular structures

9:20 AM - 9:35 AM

Co-authors: Natalie Klein (Los Alamos National Laboratory), Natalie Klein (Los Alamos National Laboratory)

machine learning chemistry

Real-time lightning forecasting leveraging spatio-temporal correlations

9:35 AM - 9:50 AM

Co-authors: Daniel Ries (Sandia National Laboratories), Daniel Ries (Sandia National Laboratories), Gavin Collins, Kellie McClernon (Sandia National Laboratories), Thom Edwards (Sandia National Laboratories)

lightning convolutional neural network generalized linear model

Sample Size Considerations in Probability of Detection Studies for Nondestructive Evaluation

9:50 AM - 10:05 AM

Co-authors: Ray de Asis, Christine Schubert Kabban (Air Force Institute of Technology), Ray de Asis

Probability of Detection Nondestructive Evaluation flaw size a-hat vs a hit/miss sample size

Semi-parametric Modeling of the Equation of State of Dissociating Materials

10:05 AM - 10:20 AM

Co-authors: Jolypich Pek, Jolypich Pek, Seiyon Lee (George Mason University), Jason Bernstein (Lawrence Livermore National Laboratory), Philip Myint (Lawrence Livermore National Laboratory)

Semi-Parametric Uncertainty Quantification Inverse Problem Material Science