首页|Dynamic Gaussian process regression for spatio-temporal data based on local clustering
Dynamic Gaussian process regression for spatio-temporal data based on local clustering
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
This paper introduces techniques in Gaussian process regression model for spatio-temporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Further-more,the shock tube problem is successfully approximated under different phenomenon complexity.
万方数据
维普
