Implementation of Physics Informed Neural Networks on Edge Device-Reference-Cited by-同舟云学术

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Implementation of Physics Informed Neural Networks on Edge Device

Published:2023-12-18 Issue: Volume:2203 Page:441-445
ISSN:
Container-title:2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)
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Author:

Zhang Xuezhi¹,Wey I-Chyn²,Xiang Maoyang³,Teo T. Hui³

Affiliation:

1. Singapore University of Technology and Design,Nano-Electronic Engineering and Design,Singapore,Singapore

2. Chang Gung University,Department of Electrical Engineering,Taiwan

3. Singapore University of Technology and Design,Engineering Product Development,Singapore,Singapore

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10387741/10387791/10387854.pdf?arnumber=10387854

Reference13 articles.

1. Artificial neural networks for solving ordinary and partial differential equations

2. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

3. Physics-informed machine learning

4. Implementation of Binarized Neural Networks in All-Programmable System-on-Chip Platforms

5. Pre-Computing Batch Normalisation Parameters for Edge Devices on a Binarized Neural Network

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