Probabilistic modeling of long-term joint wind and wave load conditions via generative adversarial network-Reference-Cited by-同舟云学术

Probabilistic modeling of long-term joint wind and wave load conditions via generative adversarial network

Published:2023-03-18 Issue:7 Volume:37 Page:2829-2847
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Song Yupeng,Hong Xu,Xiong Jiecheng,Shen Jiaxu,Xu Zekun

Funder

Natural Science Foundation of Jiangsu Province

Natural Science Research of Jiangsu Higher Education Institutions of China

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00477-023-02421-4.pdf

Reference49 articles.

1. Aas K, Czado C, Frigessi A, Bakken H (2009) Pair-copula constructions of multiple dependence. Insur Math Econ 44(2):182–198

2. Abirami S, Chitra P (2022) Regional spatio-temporal forecasting of particulate matter using autoencoder based generative adversarial network. Stoch Environ Res Risk Assess 36(5):1255–1276

3. Ang AH-S, Tang WH (2007) Probability concepts in engineering: emphasis on applications to civil and environmental engineering. Willey, Hoboken

4. Arjovsky M, Bottou L (2017) Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862

5. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Doina P, Yee Whye T (eds) The 34th international conference on machine learning. PMLR, pp 214–223