Computational Intelligence - November 2017 - 48
Knowledge Transfer
Through Machine Learning
in Aircraft Design
Alan Tan Wei Min
School of Computer Science and Engineering,
Nanyang Technological University, Singapore
Ramon Sagarna
Rolls-Royce@NTU Corporate Lab, Nanyang
Technological University, Singapore
Abhishek Gupta and Yew-Soon Ong
Rolls-Royce@NTU Corporate Lab, Nanyang
Technological University, Singapore
School of Computer Science and Engineering,
Nanyang Technological University, Singapore
Chi Keong Goh
Rolls-Royce@NTU Corporate Lab, Nanyang
Technological University, Singapore
Abstract-The modern aircraft has evolved to become an
important part of our society. Its design is multidisciplinary in
nature and is characterized by complex analyses of mutually
interdependent disciplines and large search spaces. Machine
learning has, historically, played a significant role in aircraft
design, primarily by approximating expensive physics-based
numerical simulations. In this work, we summarize the current
role of machine learning in this application domain, and highlight the opportunity of incorporating recent advances in the
field to further its impact. Specifically, regression models (or
surrogate models) that represent a major portion of the current
efforts are generally built from scratch assuming a zero prior
Digital Object Identifier 10.1109/MCI.2017.2742781
Date of publication: 12 October 2017
48
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2017
knowledge state, only relying on data from the ongoing target
problem of interest. However, due to the incremental nature of
design processes, there likely exists relevant knowledge from
various related sources that can potentially be leveraged. As
such, we present three relatively advanced machine learning
technologies that facilitate automatic knowledge transfer in
order to improve design performance. Subsequently, we demonstrate the efficacy of one of these technologies, i.e. transfer
learning, on two use cases of aircraft engine design yielding
noteworthy results. Our aim is to unveil this new application as
a well-suited arena for the salient features of knowledge transfer in machine learning to come to the fore, thereby encouraging future research efforts.
Corresponding Author:Yew-Soon Ong (Email: asysong@ntu.edu.sg)
1556-603X/17©2017IEEE
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