Detecting Defects in Sequential Inputs to Digital Twins Using Machine Learning

Detecting Defects in Sequential Inputs to Digital Twins Using Machine Learning

This article presents a method for detecting defects in sequential data inputs for digital twins (DTs) during simulations, emphasizing the importance of input validation for ensuring the accuracy and reliability of the simulation results. By thoroughly validating input data, researchers and practiti...

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Bibliographic Details
Main Authors: Nathaniel Brown, Steven Simske
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of Systems Engineering
Subjects:
Defect detection
digital twin (DT)
input validation
machine learning
sequential data
Online Access:https://ieeexplore.ieee.org/document/10675423/
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https://ieeexplore.ieee.org/document/10675423/

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