Computational framework for modeling of physical process
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.
- Inventors:
- Issue Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1987123
- Patent Number(s):
- 11580280
- Application Number:
- 16/721,588
- Assignee:
- Lawrence Livermore National Security, LLC (Livermore, CA)
- DOE Contract Number:
- AC52-07NA27344
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 12/19/2019
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Chen, Xiao, Huang, Can, Min, Liang, Thimmisetty, Charanraj, and Tong, Charles. Computational framework for modeling of physical process. United States: N. p., 2023.
Web.
Chen, Xiao, Huang, Can, Min, Liang, Thimmisetty, Charanraj, & Tong, Charles. Computational framework for modeling of physical process. United States.
Chen, Xiao, Huang, Can, Min, Liang, Thimmisetty, Charanraj, and Tong, Charles. Tue .
"Computational framework for modeling of physical process". United States. https://www.osti.gov/servlets/purl/1987123.
@article{osti_1987123,
title = {Computational framework for modeling of physical process},
author = {Chen, Xiao and Huang, Can and Min, Liang and Thimmisetty, Charanraj and Tong, Charles},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2023},
month = {2}
}
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