Website: CJEN
Deadline for intent to submit: January 2016
Deadline for full paper submission: 29 April 2016
Advances in system design principles and computational technologies enable virtually unlimited design complexity and thus require far more design decisions. Specifically, the increased complexity and flexibility in a system adds significant amounts of uncertainty associated with a larger number of design variables. Computational and theoretical challenges are abound during the quantification, representation, and management of uncertainty. It is crucial to incorporate the effects that emerge from these complexities and uncertainties in the design and manufacturing processes of engineering systems in order to regulate safety and security. With the advent of machine learning, a subfield of computer science that explores construction of models from data and then uses those models to make predictions on data, many computationally efficient frameworks, for uncertainty representation and quantification, can be explored. In the past decade, significant research has been conducted in utilizing and developing various machine-learning algorithms. These developments signify a great opportunity for uncertainty quantification and representation in the systems design process.
The aim of this special issue of JED is to bring together original articles that display the significant impact of recent developments in the areas of systems design and uncertainty quantification. Topics include, but are not limited to the following:
Deadline for intent to submit: January 2016
Deadline for full paper submission: 29 April 2016
Advances in system design principles and computational technologies enable virtually unlimited design complexity and thus require far more design decisions. Specifically, the increased complexity and flexibility in a system adds significant amounts of uncertainty associated with a larger number of design variables. Computational and theoretical challenges are abound during the quantification, representation, and management of uncertainty. It is crucial to incorporate the effects that emerge from these complexities and uncertainties in the design and manufacturing processes of engineering systems in order to regulate safety and security. With the advent of machine learning, a subfield of computer science that explores construction of models from data and then uses those models to make predictions on data, many computationally efficient frameworks, for uncertainty representation and quantification, can be explored. In the past decade, significant research has been conducted in utilizing and developing various machine-learning algorithms. These developments signify a great opportunity for uncertainty quantification and representation in the systems design process.
The aim of this special issue of JED is to bring together original articles that display the significant impact of recent developments in the areas of systems design and uncertainty quantification. Topics include, but are not limited to the following:
- Uncertainty and risk representation and quantification
- Machine learning techniques for systems design
- Systems engineering issues due to increasing complexity of modern designs
- Prediction of remaining useful life, prognostics and health management
- Reliability and robust systems design
- Designer and consumer preference modeling
- Case studies in systems design under uncertain market conditions such as environmental regulations
Publication Target Schedule:
Notification of Intent to Submit: January, 2016 (Note: email any one of the guest editors)
Full Papers Due for Review: 29th April 2016
Notification of Review Decision: 30th June, 2016
Revised Manuscript Submission: 31st August, 2016
Final Decision: 31st October, 2016
Final Manuscripts:30th November, 2016
Expected Date of Publication: February, 2017 (Vol. 28 n 2)
Submission Instructions:
Please prepare your paper following the “Instructions for Authors” available from the Journal of Engineering Design website.
Please submit your paper directly to the journal.
Guest Editors (in alphabetical order):
Seung-Kyum Choi, Ph.D.
Associate Professor, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA, schoi@me.gatech.edu.
Jiten Patel, Ph.D., P.E.
Associate, Stress Engineering Services, USA, pjiten@gmail.com.
Kok-Kwang Phoon, Ph.D.
Distinguished Professor, Dept. of Civil and Environmental Engineering, National University of Singapore, Singapore, kkphoon@nus.edu.sg.
Bruno Sudret, Ph.D.
Professor and Chair of Risk, Safety and UQ, Institute of Structural Engineering ETH Zurich, Swiss, sudret@ibk.baug.ethz.ch.
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