Validation Framework of a Digital Twin: A System Identification Approach

被引：0
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作者：
Purdue University, School of Industrial Engineering, 315 N. Grant Street, West Lafayette [1 ]
IN, United States
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来源：
INCOSE Int. Sym. | 2024年 / 1卷 / 249-267期
关键词：
The constant improvement and developments in Artificial Intelligence/Machine learning models coupled with increased computing power have led to the incorporation of AI/ML for simulating learning and problem-solving in simple and complex engineering systems. This latent uncertainty and unpredictable characteristics of AI-enabled systems challenges engineers and industry stakeholders who care about ensuring the right systems are built (system validation). Digital Twins are an excellent example of such AI-enabled systems due to their data-dependent nature when tasked with replicating; monitoring; and updating physical assets for structural health monitoring and control. However; Digital Twins' system validation has not been well-researched. This study delves into existing research and frameworks for validating Digital Twins and proposes a novel model-centric validation framework based on system identification techniques. As a case study; we apply this model-centric validation framework towards partially validating a Digital Twin for a single-heat-pipe test article for a Microre-actor Agile Non-nuclear Experimental Testbed. Copyright © 2024 by Ibukun Phillips and Robert Kenley. Permission granted to INCOSE to publish and use;
D O I：
10.1002/iis2.13145
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