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Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

https://doi.org/10.2139/ssrn.5069788
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/ghg.1433openalex-snapshot
An overview of the Illinois Basin – Decatur Project
inferredcites →https://doi.org/10.1002/ghg.1467openalex-snapshot
Illinois Basin – Decatur Project
inferredcites →https://doi.org/10.1016/j.compbiomed.2023.107676openalex-snapshot
Learning reduced-order models for cardiovascular simulations with graph neural networks
inferredcites →https://doi.org/10.1017/9781316480724.020openalex-snapshot
Illinois Basin–Decatur Project
inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-snapshot
The Graph Neural Network Model
inferredcites →https://doi.org/10.1109/tnnls.2020.2978386openalex-snapshot
A Comprehensive Survey on Graph Neural Networks
inferredcites →https://doi.org/10.1145/3534678.3539045openalex-snapshot
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
inferredcites →https://doi.org/10.2139/ssrn.4272874openalex-snapshot
A Machine Learning-Based Field-Scale Reservoir Analysis Method
inferredcites →https://doi.org/10.48550/arxiv.1704.01212openalex-snapshot
Neural Message Passing for Quantum Chemistry
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