Impact of the Complexity of the Geometry in an Analytical Solution Used to Train a Deep Learning Network<sup>*</sup>-Reference-Cited by-同舟云学术

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Impact of the Complexity of the Geometry in an Analytical Solution Used to Train a Deep Learning Network^*

Published:2023-12-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology
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Author:

Carluccio Giuseppe¹,Montin Eros¹,Lattanzi Riccardo¹,Collins Christopher¹

Affiliation:

1. New York University,Department of Radiology,New York,NY 10016,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10404092/10404107/10404125.pdf?arnumber=10404125

Reference11 articles.

1. MRSaiFE: An AI-Based Approach Towards the Real-Time Prediction of Specific Absorption Rate

2. An analytical and a Deep Learning model for solving the inverse kinematic problem of an industrial parallel robot

3. A deep learning model for the estimation of RF field trained from an analytical solution

4. Scattering by a lossy dielectric circular cylindrical multilayer numerical values

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Impact of Multiple Layers of High-Permittivity Materials on the EM Fields Transmission for Magnetic Resonance Imaging Applications;2024 IEEE INC-USNC-URSI Radio Science Meeting (Joint with AP-S Symposium);2024-07-14

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