Weak Constraint Gaussian Processes for optimal sensor placement

Tolga Hasan Dur, Rossella Arcucci*, Laetitia Mottet, Miguel Molina Solana, Christopher Pain, Yi-Ke Guo

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    8 Citations (Scopus)

    Abstract

    We present a Weak Constraint Gaussian Process (WCGP) model to integrate noisy inputs into the classical Gaussian Process (GP) predictive distribution. This model follows a Data Assimilation approach (i.e. by considering information provided by observed values of a noisy input in a time window). Due to the increased number of states processed from real applications and the time complexity of GP algorithms, the problem mandates a solution in a high performance computing environment. In this paper, parallelism is explored by defining the parallel WCGP model based on domain decomposition. Both a mathematical formulation of the model and a parallel algorithm are provided. We use the algorithm for an optimal sensor placement problem. Experimental results are provided for pollutant dispersion within a real urban environment.

    Original languageEnglish
    Article number101110
    JournalJournal of Computational Science
    Volume42
    DOIs
    Publication statusPublished - Apr 2020

    Scopus Subject Areas

    • Theoretical Computer Science
    • Computer Science(all)
    • Modelling and Simulation

    User-Defined Keywords

    • Big data
    • Data assimilation
    • Gaussian Processes
    • Parallel algorithms
    • Sensor placement

    Fingerprint

    Dive into the research topics of 'Weak Constraint Gaussian Processes for optimal sensor placement'. Together they form a unique fingerprint.

    Cite this