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.

中文翻译：

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工程设计图模型：基于数据集和其他知识来源的模型编码和保真度评估

从具有有效因果顺序的小型数据集中自动提取知识是当前机器学习中最先进方法的挑战。提取其他类型的知识很重要，但对于数据稀缺且难以收集的多个工程领域而言具有挑战性。本研究旨在通过提出一个基于机器学习的建模框架来解决这个问题，该框架利用从数据样本中记录的变量的基本单元中可用的知识，在早期设计阶段开发简约、可解释和基于图形的仿真模型。使用在流体中移动的球体的工程设计案例研究来举例说明所开发的方法。对于感兴趣的系统，通过 (1) 使用从数据集中自动选择变量和 (2) 将自动提取与有关与系统变量相关的功能和维度同质性的补充知识相结合，可以生成两种类型的复杂模型。研究了设计、数据、模型和仿真规范对模型保真度的影响。该研究讨论了保真度、变量、函数和可用知识之间的相互关系。该研究通过提出用于将数据转换为生成模型的可测量保真度水平的标准化建模方法的前提，为保真度测量理论的发展做出了贡献。