Publications ⇤
Spatio-Temporal Weathering Predictions in the Sparse Data Regime with Gaussian Processes
Giovanni De Felice, Vladimir Gusev, John Goulermas, Michael Gaultois, Matthew Rosseinsky, Catherine Vincent Gauvin
Tags:
spatio-temporal
Venue: AI4Science NeurIPS 2022
We investigate the problem of predicting the expected lifetime of a material in different climatic conditions from a few observations in sparsely located testing facilities. We propose a Spatio-Temporal adaptation of Gaussian Process Regression that takes full advantage of high-quality satellite data by performing an interpolation directly in the space of climatological time-series. We illustrate our approach by predicting gloss retention of industrial paint formulations. Furthermore, our model provides uncertainty that can guide decision-making and is applicable to a wide range of problems.