Gaussian process metamodeling for experiments with manipulating factors

11/03/2019
by   Chiwoo Park, et al.
0

This paper presents a new Gaussian process (GP) metamodeling approach for predicting the outcome of a physical experiment for a given input factor setting where some of the input factors are controlled by other manipulating factors. Particularly, we study the case where the control precision is not very high, so the input factor values vary significantly even under the same setting of the corresponding manipulating factors. Due to this variability, the standard GP metamodeling that directly relates the manipulating factors to the experimental outcome does not provide a great predictive power on the outcome. At the same time, the GP model relating the main factors to the outcome directly is not appropriate for the prediction purpose because the main factors cannot be accurately set as planned for a future experiment. We propose a two-tiered GP model, where the bottom tier relates the manipulating factors to the corresponding main factors with potential biases and variability, and the top tier relates the main factors to the experimental outcome. Our two-tier model explicitly models the propagation of the control uncertainty to the experimental outcome through the two GP modeling tiers. We present the inference and hyper-parameter estimation of the proposed model. The proposed approach is tested on a motivating example of a closed-loop autonomous research system for carbon nanotube growth experiments, and the test results are reported with the comparison to a benchmark method, i.e. a standard GP model.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/07/2020

Nonnegativity-Enforced Gaussian Process Regression

Gaussian Process (GP) regression is a flexible non-parametric approach t...
research
03/22/2023

Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control

Algorithm aversion occurs when humans are reluctant to use algorithms de...
research
02/22/2023

Factors Influencing Autonomously Generated 3D Geophysical Spatial Models

Understanding the contribution of geophysical variables is vital for ide...
research
01/24/2022

Design Strategies and Approximation Methods for High-Performance Computing Variability Management

Performance variability management is an active research area in high-pe...
research
11/22/2020

Robust Gaussian Process Regression Based on Iterative Trimming

The model prediction of the Gaussian process (GP) regression can be sign...
research
06/24/2022

Gaussian Process-based calculation of look-elsewhere trials factor

In high-energy physics it is a recurring challenge to efficiently and pr...
research
06/11/2019

Behavioral Switching Loss Modeling of Inverter Modules

This paper presents a new behavioral model for switching power loss eval...

Please sign up or login with your details

Forgot password? Click here to reset