A causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis

Author:

Adombi Adoubi Vincent De Paul,Chesnaux RomainORCID,Boucher Marie-AmélieORCID,Braun MarcoORCID,Lavoie JulietteORCID

Funder

NSERC

FRQNT

Publisher

Elsevier BV

Reference90 articles.

1. Comparing numerical modelling, traditional machine learning and theory-guided machine learning in inverse modeling of groundwater dynamics: A first study case application;Adombi;Mendeley Data,2022

2. Code: Proposal of a causal theory-guided Deep Learning formulation for groundwater flow modeling;Adombi;Zenado,2023

3. Review: Theory-guided machine learning applied to hydrogeology—state of the art, opportunities and future challenges;Adombi;Hydrgeol. J.,2021

4. A review of surrogate models and their application to groundwater modeling;Asher;Water Resour. Res.,2015

5. Enhancement of groundwater-level prediction using an integrated machine learning model optimized by whale algorithm;Banadkooki;Nat. Resour. Res.,2020

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