Deep Gaussian Process-Based Bayesian Inference for Contaminant Source Localization

Deep Gaussian Process-Based Bayesian Inference for Contaminant Source Localization

This paper proposes a Bayesian framework for localization of multiple sources in the event of accidental hazardous contaminant release. The framework assimilates sensor measurements of the contaminant concentration with the integrated multizone computational fluid dynamics (multizone-CFD)-based cont...

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Bibliographic Details
Main Authors: Young-Jin Park, Piyush M. Tagade, Han-Lim Choi
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Bayesian methods
contaminant source localization
deep matrix-variate Gaussian processes
Gaussian processes
Monte Carlo methods
supervised learning
Online Access:https://ieeexplore.ieee.org/document/8449923/

Internet

https://ieeexplore.ieee.org/document/8449923/

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