Poster
in
Workshop: AI for Science: Progress and Promises
Spatio-Temporal Weathering Predictions in the Sparse Data Regime with Gaussian Processes
Giovanni De Felice · Vladimir Gusev · John Goulermas · Michael Gaultois · Matthew Rosseinsky · Catherine Gauvin
Keywords: [ spatio-temporal machine learning ] [ sparse data ] [ materials weathering ] [ Gaussian Processes ]
We investigate the problem of predicting the expected lifetime of a material in different climatic conditions from a few observations in sparse 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.