Gaussian Process Regression — MecaNano Tutorial Series

This tutorial introduces Gaussian Process Regression (GPR), a non-parametric probabilistic approach widely adopted for interpolation, regression, and uncertainty quantification in materials science. The session explores both 1D and 2D inference, implemented with pyro.contrib.gp and PyTorch, with examples crafted for clarity and practical use.

Tutorial Slides

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1. Overview of Gaussian Processes

2. Univariate GP Regression

3. Bivariate GP Regression

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