Deep kernel learning approach to engine emissions modeling

We apply deep kernel learning (DKL), which can be viewed as a combination of a Gaussian process (GP) and a deep neural network (DNN), to compression ignition engine emissions and compare its performance to a selection of other surrogate models on the same dataset. Surrogate models are a class of com...

詳細記述

書誌詳細

出版年:	Data-Centric Engineering
主要な著者:	Changmin Yu, Marko Seslija, George Brownbridge, Sebastian Mosbach, Markus Kraft, Mohammad Parsi, Mark Davis, Vivian Page, Amit Bhave
フォーマット:	論文
言語:	英語
出版事項:	Cambridge University Press 2020-01-01
主題:	Deep kernel learning emissions surrogate models Gaussian processes internal combustion engines
オンライン･アクセス:	https://www.cambridge.org/core/product/identifier/S2632673620000040/type/journal_article