Graph models for engineering design: Model encoding, and fidelity evaluation based on dataset and other sources of knowledge

Eric Coatanéa, Hari Nagarajan, Hossein Mokhtarian, Di Wu, Suraj Panicker, Andrés Morales-Forero, Samuel Bassetto

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-The-Art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework leveraging the knowledge available in fundamental units of the variables recorded from data samples, to develop parsimonious, explainable, and graph-based simulation models during the early design stages. The developed approach is exemplified using an engineering design case study of a spherical body moving in a fluid. For the system of interest, two types of intricated models are generated by (1) using an automated selection of variables from datasets and (2) combining the automated extraction with supplementary knowledge about functions and dimensional homogeneity associated with the variables of the system. The effect of design, data, model, and simulation specifications on model fidelity are investigated. The study discusses the interrelationships between fidelity levels, variables, functions, and the available knowledge. The research contributes to the development of a fidelity measurement theory by presenting the premises of a standardized, modeling approach for transforming data into measurable level of fidelities for the produced models. This research shows that structured model building with a focus on model fidelity can support early design reasoning and decision making using for example the dimensional analysis conceptual modeling (DACM) framework.

Original languageEnglish
Article numbere6
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume37
DOIs
Publication statusPublished - 20 Feb 2023
Publication typeA1 Journal article-refereed

Keywords

  • Causality
  • dimensional analysis conceptual modeling (DACM)
  • machine learning
  • model fidelity
  • oriented graphs

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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