Probabilistic learning and updating of a digital twin for composite material systems-Reference-Cited by-同舟云学术

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Probabilistic learning and updating of a digital twin for composite material systems

Published:2020-06-29 Issue: Volume: Page:
ISSN:0029-5981
Container-title:International Journal for Numerical Methods in Engineering
language:en
Short-container-title:Int J Numer Methods Eng

Author:

Ghanem Roger¹^ORCID,Soize Christian²^ORCID,Mehrez Loujaine¹,Aitharaju Venkat³

Affiliation:

1. Viterbi School of EngineeringUniversity of Southern California Los Angeles California USA

2. Laboratoire Modélisation et Simulation Multi EchelleUniversité Gustave Eiffel Marne‐la‐Vallée France

3. GM R&D Technical CenterGeneral Motors Company Warren Michigan USA

Publisher

Wiley

Subject

Applied Mathematics,General Engineering,Numerical Analysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nme.6430

Reference22 articles.

1. Shaping the digital twin for design and production engineering

2. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

3. Automatic fault detection for laser powder‐bed fusion using semi‐supervised machine learning;Okaro IA;Addit Manuf,2019

4. The role of process variables in laser-based direct metal solid freeform fabrication

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