Scalable Gaussian Processes for Data-Driven Design Using Big Data With Categorical Factors

Volume: 144, Issue: 2
Published: Sep 15, 2021
Abstract
Scientific and engineering problems often require the use of artificial intelligence to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners, they have difficulties in accommodating big data sets, categorical inputs, and multiple responses, which has become a common challenge for a growing number of data-driven design applications. In this paper, we propose a GP...
Paper Details
Title
Scalable Gaussian Processes for Data-Driven Design Using Big Data With Categorical Factors
Published Date
Sep 15, 2021
Volume
144
Issue
2
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