Gaussian Process Regression for Surface Interpolation
2022.07.06, Zhiqiao Dong & Manan Mehta, University of Illinois Urbana-Champaign
Filtered Kriging Lab tool can be found at: https://nanohub.org/tools/fkriging
Part of Hands-on Data Science and Machine Learning Training Series at: https://nanohub.org/groups/ml/handson...
Table of contents below.
Gaussian process regression (GPR) is a nonparametric regression method with widespread applications in various scientific and engineering fields. In manufacturing, it has been used for surface interpolation that generates high-resolution surface estimations from coarser measurement data. This tutorial will introduce the fundamentals of GPR and its application to surface interpolation. We will also introduce a new technique called filtered kriging (FK), which uses a pre-filter to improve interpolation performance. The FK method will be illustrated using periodic surfaces manufactured by two photon lithography.
Table of Contents:
00:00 Gaussian Process Regression for Surface Interpolation
00:53 A Motivating Example from Nanomanufacturing
02:06 Motivation for Spatial Interpolation
03:13 Spatial Interpolation
04:13 1-D Example: Motivation
06:31 1-D Example: Inference on New Data
08:42 1-D Example: Inference on New Data
09:51 Gaussian Process (GP)
10:40 Covariance (Kernal) for GPR
12:10 GPR Workflow
14:00 Filtered Kriing Lab Demo
22:49 Spatial Interpolation Based on GPR
25:04 Spatial Interpolation Based on GPR
25:26 Spatial Interpolation Based on GPR
26:08 Spatial Interpolation Based on GPR
26:26 Conventional GPR-Based Methods
27:21 Filtered Kriging
28:17 Improved Covariance Modeling with FK
29:38 Improved Covariance Modeling with FK
30:39 Improved Covariance Modeling with FK
31:54 Improved Covariance Modeling with FK
33:29 Tutorial to Filtered Kriging for Spatial Interpolaton
This presentation and related downloads can be found at: https://nanohub.org/resources/36189
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