Development of graphical user interface for design of experiments via Gaussian process regression and its case study

ABSTRACT

Bayesian optimization, coupled with Gaussian process regression and acquisition functions, has proven to be a powerful tool in the field of experimental design. Nevertheless, it demands a profound proficiency in software programming, machine learning, and statistical concepts. This steep learning curve presents a substantial obstacle when implementing Bayesian optimization for experimental design. In order to overcome this challenge, a user-friendly graphical interface for Gaussian process regression and acquisition functions is proposed. This accessible tool can be readily accessed via web browsers, courtesy of the established CADS platform (available at https://cads.eng.hokudai.ac.jp/). Thus, the interface offers to perform Bayesian optimization without any programming or any extensive prior knowledge about Bayesian optimization and machine learning.

GRAPHICAL ABSTRACT

IMPACT STATEMENT

A user-friendly graphical interface for Bayesian optimization with Gaussian process　regression and acquisition functions is proposed. The interface offers to perform Bayesian optimization without any programming or any extensive prior knowledge about Bayesian optimization and machine learning.

KEYWORDS:

• Machine learning
• graphical user interface
• Bayesian optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is funded by Japan Science and Technology Agency(JST) CREST Grant Number [JPMJCR17P2], ERATO grant number [JPMJER1903], JST Mirai Program Grant Number [JPMJMI22G4], JSPS KAKENHI Grant-in-Aid for Scientific Research (B) Grant Number [JP23H01762].