Traditional sampling techniques (grid vs random vs sobol vs latin hypercube)

Welcome to video #1 of the Adaptive Experimentation series, presented by graduate student Sterling Baird @sterling-baird at the 18th IEEE Conference on eScience in Salt Lake City, UT (Oct 10-14, 2022). In this video, Sterling introduces the concept of adaptive experimentation and covers traditional sampling approaches, including grid, random, Latin hypercube, and Sobol sampling. He also discusses the use of discrepancy as a metric for performance evaluation. Don't miss the next installment in this informative series on experimental optimization. Github link to jupyter notebook https://github.com/sparks-baird/self-driving-lab-demo/blob/main/notebooks/escience/1.0-traditional-doe-vs-bayesian.ipynb next video in series: https://youtu.be/Evua529dAgc 0:00 introduction to adaptive experimentation 2:09 Comparing grid/random search with quasi-random search with adaptive experimentation approaches (grid vs human intuition) 4:40 traditional optimization jupyter notebook tutorial 6:14 grid sampling 7:45 latin hypercube and sobol sampling 9:36 comparing different sampling 11:05 discrepancy comparison in low and high dimensional data