Identification of physical processes and unknown parameters of 3D groundwater contaminant problems via theory-guided U-net-Reference-Cited by-同舟云学术

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Identification of physical processes and unknown parameters of 3D groundwater contaminant problems via theory-guided U-net

Published:2023-11-17 Issue: Volume: Page:
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

He Tianhao,Chang Haibin,Zhang Dongxiao

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/s00477-023-02604-z.pdf

Reference52 articles.

1. Brunton SL, Proctor JL, Kutz JN, Bialek W (2016) Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci PNAS 113(15):3932–3937. https://doi.org/10.1073/pnas.1517384113

2. Cao T, Zeng X, Wu J, Wang D, Sun Y, Zhu X, Long Y et al (2019) Groundwater contaminant source identification via Bayesian model selection and uncertainty quantification. Hydrogeol J 27(8):2907–2918. https://doi.org/10.1007/s10040-019-02055-3

3. Chang H, Zhang D (2019) Identification of physical processes via combined data-driven and data-assimilation methods. J Comput Phys 393:337–350. https://doi.org/10.1016/j.jcp.2019.05.008

4. Chang H, Liao Q, Zhang D (2017) Surrogate model based iterative ensemble smoother for subsurface flow data assimilation. Adv Water Resour 100:96–108. https://doi.org/10.1016/j.advwatres.2016.12.001

5. Chen Y, Oliver DS (2013) Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Comput Geosci 17(4):689–703. https://doi.org/10.1007/s10596-013-9351-5

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