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 journalArticlepeer-review

5 Citations (Scopus)


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
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


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