Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks-Reference-Cited by-同舟云学术

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Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks

Published:2024 Issue: Volume:12 Page:17782-17792
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Baeuerle Simon¹^ORCID,Gebhardt Marius²,Barth Jonas²,Mikut Ralf¹^ORCID,Steimert Andreas³

Affiliation:

1. Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany

2. Robert Bosch GmbH, Reutlingen, Germany

3. Bosch Center for Artificial Intelligence (BCAI), Robert Bosch GmbH, Renningen, Germany

Funder

Karlsruhe Institute of Technology (KIT)-Publication Fund of KIT

Helmholtz Associations Initiative and Networking Fund through the Helmholtz Artificial Intelligence Cooperation Unit

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10414972.pdf?arnumber=10414972

Reference28 articles.

1. CAD-to-real: enabling deep neural networks for 3D pose estimation of electronic control units

2. Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

3. Digital Twin Requirements in the Context of Industry 4.0

4. Effects of thermal interface materials (solders) on thermal performance of a microelectronic package;Ekpu

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