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.

Zhiqiao Dong is a Ph.D. student in the Department of Mechanical Science and Engineering at University of Illinois Urbana-Champaign. He received his B.E. in Automation and B.S. in Mathematics from the University of Science and Technology of China in 2019. His research interests include vision-based monitoring and control of manufacturing processes. He is currently working on modeling and interpolation of spatial processes.

Manan Mehta is a Ph.D. student in the Department of Mechanical Science and Engineering at University of Illinois Urbana-Champaign. He received his B.E. (Hons.) in Mechanical Engineering from Birla Institute of Technology and Science, Pilani, India, in 2019. He is broadly interested in applying statistical and machine learning techniques to smart manufacturing applications. His research has spanned a range of topics including Gaussian processes, multi-task learning, heuristic optimization, and federated learning.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Zhiqiao Dong, Manan Mehta (2022), "Gaussian Process Regression for Surface Interpolation," https://nanohub.org/resources/36189.

  BibTex | EndNote

1. Gaussian process regression (GPR)
2. hands on
3. lithography
4. machine learning
5. nanoelectronics
6. nanomanufacturing
7. NCN Group - Machine Learning Hands-on
8. regression
9. surface interpolation