Discovering the Unknowns: A First Step
Methodology
2023-10-12 v1
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.
Cite
@article{arxiv.2310.07016,
title = {Discovering the Unknowns: A First Step},
author = {V. Roshan Joseph and William E. Lewis and Henry S. Yuchi and Kathryn A. Maupin},
journal= {arXiv preprint arXiv:2310.07016},
year = {2023}
}