Validation Framework of a Digital Twin: A System Identification Approach

被引：0

作者：

Phillips, Ibukun ^{[1
]}

Kenley, C. Robert ^{[1
]}

机构：

[1] Purdue University, School of Industrial Engineering, 315 N. Grant Street, West Lafayette,IN, United States

来源：

INCOSE International Symposium | 2024年 / 34卷 / 01期

关键词：

Adversarial machine learning - Contrastive Learning;

D O I：

10.1002/iis2.13145

中图分类号：

学科分类号：

摘要：

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.

引用

页码：249 / 267