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.

我们提出了一个弱约束高斯过程（WCGP）模型，将噪声输入整合到经典的高斯过程（GP）预测分布中。该模型遵循数据同化方法（即，通过考虑由时间窗口中的噪声输入的观察值提供的信息）。由于从实际应用程序处理的状态数量增加以及GP算法的时间复杂性，该问题要求在高性能计算环境中提供解决方案。在本文中，通过定义基于域分解的并行WCGP模型来探索并行性。提供了模型的数学公式和并行算法。我们将算法用于最佳传感器放置问题。提供了在实际城市环境中污染物扩散的实验结果。