The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Feb. 27, 2024
Filed:
Feb. 13, 2023
Lawrence Livermore National Security, Llc, Livermore, CA (US);
Virginia Polytechnic Institute and State University, Blacksburg, VA (US);
Xiao Chen, Tracy, CA (US);
Can Huang, Livermore, CA (US);
Liang Min, Pleasanton, CA (US);
Charanraj Thimmisetty, Dublin, CA (US);
Charles Tong, Danville, CA (US);
Yijun Xu, Nanjing, CN;
Lamine Mili, New Alexandria, VA (US);
LAWRENCE LIVERMORE NATIONAL SECURITY, LLC, , CA (US);
VIRGINIA TECH INTELLECTUAL PROPERTIES, INC., , VA (US);
Abstract
Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N. The method includes generating a set of approximated system variables corresponding to the set of measurements based on the numerical model.