Physics-informed surrogate modeling for supporting climate resilience at groundwater contamination sites

Meray, Aurelien; Wang, Lijing; Kurihana, Takuya; Mastilovic, Ilijana; Praveen, Satyarth; Xu, Zexuan; Memarzadeh, Milad; Lavin, Alexander; Wainwright, Haruko

doi:10.1016/j.cageo.2023.105508

Computers & Geosciences

2024

DOI: 10.1016/j.cageo.2023.105508

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Physics-informed surrogate modeling for supporting climate resilience at groundwater contamination sites

Aurelien Meray,

Lijing Wang,

Takuya Kurihana

et al.

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Cited by 5 publications

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An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

Ayoub,

Wainwright,

Wang

et al. 2024

Stoch Environ Res Risk Assess

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Accurate real-time forecasts of atmospheric plume behavior are crucial for effective management of environmental release incidents. However, the computational demands of weather simulations and particle transport codes limit their applicability during emergencies. In this study, we employ a U-Net enhanced Fourier Neural Operator (U-FNO) to statistically emulate the calculations of the WSPEEDI dose forecasting numerical simulator, using pre-calculated ensemble simulations. The developed emulator is capable of effectively simulating any radioactive-release scenario and generating the time series of dose distribution in the environment 4000 times faster than the numerical simulator, while still maintaining high accuracy. It predicts the plume direction, extent, and dose-rate magnitudes using initial- and boundary-condition meteorological data as input. The speed and efficiency of this framework offers a powerful tool for swift decision-making during emergencies, facilitating risk-informed protective actions, evacuation execution, and zone delineation. Its application extends to various contaminant release and transport problems, and can be instrumental in engineering tasks requiring uncertainty quantification (UQ) for environmental risk assessment.

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An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

Ayoub,

Wainwright,

Wang

et al. 2024

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

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A metamodel for estimating time-dependent groundwater-induced subsidence at large scales

Haaf,

Wikby,

Abed

et al. 2024

Engineering Geology

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Efficient river hydrodynamics modelling in realistic river systems using a Fourier neural operator-based network

Pang

2024

Journal of Hydrology

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Physics-informed surrogate modeling for supporting climate resilience at groundwater contamination sites

Cited by 5 publications

References 16 publications

An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

An enhanced fourier neural operator surrogate for radioactive plume transport forecasting

A metamodel for estimating time-dependent groundwater-induced subsidence at large scales

Efficient river hydrodynamics modelling in realistic river systems using a Fourier neural operator-based network

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