On the Uncertainty Analysis of the Data-Enabled Physics-Informed Neural Network for Solving Neutron Diffusion Eigenvalue Problem-Reference-Cited by-同舟云学术

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On the Uncertainty Analysis of the Data-Enabled Physics-Informed Neural Network for Solving Neutron Diffusion Eigenvalue Problem

Published:2023-09-14 Issue: Volume: Page:1-22
ISSN:0029-5639
Container-title:Nuclear Science and Engineering
language:en
Short-container-title:Nuclear Science and Engineering

Author:

Yang Yu¹,Gong Helin²³,He Qiaolin¹,Yang Qihong¹,Deng Yangtao¹,Zhang Shiquan¹

Affiliation:

1. Sichuan University, School of Mathematics, Chengdu, 610065, China

2. Shanghai Jiao Tong University, Paris Elite Institute of Technology, Shanghai, 200240, China

3. Nuclear Power Institute of China, Chengdu, 610041, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Innovation Funds of CNNC

Publisher

Informa UK Limited

Subject

Nuclear Energy and Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00295639.2023.2236840

Reference48 articles.

1. Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

2. Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models

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1. Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint;Computer Methods in Applied Mechanics and Engineering;2024-12

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