Developing a cost-effective emulator for groundwater flow modeling using deep neural operators

Author:

Taccari Maria LuisaORCID,Wang HeORCID,Goswami SomdattaORCID,Florio Mario DeORCID,Nuttall Jonathan,Chen Xiaohui,Jimack Peter K.

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference39 articles.

1. Learning data-driven discretizations for partial differential equations;Bar-Sinai;Proc. Natl. Acad. Sci.,2019

2. Deep neural operators can predict the real-time response of floating offshore structures under irregular waves;Cao,2023

3. Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems;Chen;IEEE Trans. Neural Netw.,1995

4. Splicing recharge and groundwater flow models in the environment agency national groundwater modelling system;Farrell;Geol. Soc. Lond. Spec. Publ.,2017

5. Finite difference modeling course for groundwater flow,2017

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