MULTI-TASK MULTI-FIDELITY MACHINE LEARNING FOR RELIABILITY-BASED DESIGN WITH PARTIALLY OBSERVED INFORMATION

Yanwen Xu, Hao Wu, Zheng Liu, Pingfeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In complex engineering systems, assessing system performance and underlying failure mechanisms with respect to uncertain variables requires repeated testing, which is often limited by test capacity and computational budget and fails to accurately capture the complex system's high-dimensional nature. A method that can efficiently use information that is partially available from various sources is thus urgently needed for complex system design. This paper presents a multi-fidelity surrogate modeling strategy that efficiently utilizes partially observed information (POI) from various sources, including data with different fidelity and dimensionality. Additionally, in reliability analysis and design optimization tasks, multiple constraints must be evaluated concurrently for each design point. However, as the complexity of systems increases, the number of constraints grows, resulting in a rapid increase in computational effort. Therefore, a multi-fidelity multi-task surrogate modeling framework with POI was proposed to aid in the development of surrogate models, which increases the effectiveness of reliability analysis. The proposed multi-fidelity multi-task machine learning (MFMT-ML) model utilizes a Bayesian framework, which significantly improves the predictive model's performance and provides uncertainty quantification of the prediction. It also offers premium features such as using multi-fidelity sources of data points and POI, allowing simultaneous evaluation of multiple constraints through a single test, and offering a highly accurate and efficient reliability-based design optimization framework through knowledge sharing. By incorporating partially observed information from various sources, our approach offers a promising avenue for improving system performance prediction accuracy and efficiency while reducing the cost and complexity of complex system design.

Original languageEnglish (US)
Title of host publication49th Design Automation Conference (DAC)
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791887318
DOIs
StatePublished - 2023
EventASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023 - Boston, United States
Duration: Aug 20 2023Aug 23 2023

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume3B

Conference

ConferenceASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Country/TerritoryUnited States
CityBoston
Period8/20/238/23/23

Keywords

  • Design Optimization
  • Machine Learning
  • Multi-fidelity Model
  • Multitask Learning
  • Reliability Analysis

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

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