Topic: Domain Knowledge-Guided Machine Learning
Contents
Host
Prof. A.H.W. Ngan
Department of Materials Science and Engineering, University of Hong Kong, Hong Kong, China.
Prof. Ngan was awarded the Croucher Senior Research Fellowship, and he was elected to the Hong Kong Academy of Engineering Sciences. His research-related honours include the prestigious Rosenhain Medal and Prize from the Institute of Materials, Minerals and Mining, U.K., in 2007 – he is the only non-British national so far to have received this award since its establishment in 1951.
Speaker
Prof. Tong-Yi Zhang
The Hong Kong University of Science and Technology (Guangzhou), Guangdong, China.
Materials Genome Institute, Shanghai University, Shanghai, China.
Materials Genome Institute, Shanghai University, Shanghai, China.
Prof. Tong-Yi Zhang is an academician of the Chinese Academy of Sciences, a Fellow of the Hong Kong Academy of Engineering Sciences, the founding dean of Materials Genome Institute, Shanghai University, and the founding director of the Materials Genome Engineering division in the Chinese Materials Research Society (CMRS). He joined the Hong Kong University of Science and Technology (Guangzhou) in 2022. His current research interests are Materials Genome Engineering and Materials/mechanics Informatics.
Abstract
This presentation first briefly introduces the concept of materials/mechanics informatics. Materials/mechanics informatics is growing extremely fast by integrating machine learning with materials/mechanics science and engineering, where techniques, tools, and theories drawn from the emerging fields such as data science, internet, computer science and engineering, and digital technologies, are applied to the materials/mechanics science and engineering to accelerate materials/mechanics, products and manufacturing innovations.
Then, this presentation reports a domain knowledge-guided machine learning strategy and demonstrate it by studying the oxidation behaviours of ferritic-martensitic steels in supercritical water. This strategy leads to the development of a formula with high generalization and accurate prediction power, which is most desirable to science, technology, and engineering.
Then, this presentation reports a domain knowledge-guided machine learning strategy and demonstrate it by studying the oxidation behaviours of ferritic-martensitic steels in supercritical water. This strategy leads to the development of a formula with high generalization and accurate prediction power, which is most desirable to science, technology, and engineering.
Presentation
Prof. Tong-Yi Zhang
Topic: Domain Knowledge-Guided Machine Learning
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