Work

Discovering the Unknowns: A First Step

Year: 2024

Type: article

Abstract: . This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently estimate the large number of parameters in the proposed statistical model. The method is tested on a realistic dataset generated using a one-dimensional implementation of a Magnetized Liner Inertial Fusion (MagLIF) simulation model, and encouraging results are obtained.

Source: SIAM/ASA Journal on Uncertainty Quantification

DOI: 10.1137/23m159874x

Authors: V. Roshan Joseph, William E. Lewis, Henry S. Yuchi, Kathryn A. Maupin

Institutions: Georgia Institute of Technology, Sandia National Laboratories

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, National Nuclear Security Administration, Laboratory Directed Research and Development, Sandia National Laboratories