Physics Compliance as a Metric for Neural Network Uncertainty-Reference-Cited by-同舟云学术

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Physics Compliance as a Metric for Neural Network Uncertainty

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Symposium Series on Computational Intelligence (SSCI)
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Author:

Ou Yang Zhong Liang¹,Jiang Yang²,Wong Jian Cheng³,Chiu Pao-Hsiung³,Zhao Weijiang²,Dao My Ha³,Ooi Chin Chun²

Affiliation:

1. Institute of Infocomms Research,Dept of Machine Intellection,Singapore,Singapore

2. Institute of High Performance Computing,Dept of Electronics & Photonics,Singapore,Singapore

3. Institute of High Performance Computing,Dept of Fluid Dynamics,Singapore,Singapore

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10022049/10022014/10022196.pdf?arnumber=10022196

Reference25 articles.

1. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

2. Hyperparameter ensembles for robustness and uncertainty quantification;wenzel;Advances in neural information processing systems,2020

3. Physics-informed machine learning

4. Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

5. Simple and scalable predictive uncertainty estimation using deep ensembles;lakshminarayanan;Advances in neural information processing systems,2017

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