Matches in SemOpenAlex for { <https://semopenalex.org/work/W4297396827> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W4297396827 abstract "Direct pore-scale simulations of fluid flow through porous media are computationally expensive to perform for realistic systems. Previous works have demonstrated using the geometry of the microstructure of porous media to predict the velocity fields therein based on neural networks. However, such trained neural networks do not perform well for unseen porous media with a large degree of heterogeneity. In this study we propose that incorporating a coarse velocity field in the input of neural networks is an effective way to improve the prediction performance. The coarse velocity field can be simulated with a low computational cost and provides global information to regularize the ill-posedness of the learning problem, which is usually caused by the use of local geometries due to the computational resource constraints. We show that incorporating the coarse-mesh velocity field significantly improves the prediction accuracy of the fine-mesh velocity field by comparison to the prediction that relies on geometric information alone, especially for the porous medium with a large interior vuggy pore space. We also show the flexibility of training the network in using coarse velocity fields with various resolutions. The results suggest that even using coarse velocity field with a very low resolution, the predictions are still enhanced and close to the ground truths. The feasibility of the method is further demonstrated by testing the trained network on real rocks. This study highlights the merits of incorporating a coarse-mesh velocity field into the input for neural networks, which provides global, physics-based information for the model, thereby improving the model's generalization capability." @default.
- W4297396827 created "2022-09-28" @default.
- W4297396827 creator A5064548129 @default.
- W4297396827 creator A5077410670 @default.
- W4297396827 creator A5080868374 @default.
- W4297396827 creator A5083262924 @default.
- W4297396827 date "2021-09-20" @default.
- W4297396827 modified "2023-09-24" @default.
- W4297396827 title "Neural Network Based Pore Flow Field Prediction in Porous Media Using Super Resolution" @default.
- W4297396827 doi "https://doi.org/10.48550/arxiv.2109.09863" @default.
- W4297396827 hasPublicationYear "2021" @default.
- W4297396827 type Work @default.
- W4297396827 citedByCount "0" @default.
- W4297396827 crossrefType "posted-content" @default.
- W4297396827 hasAuthorship W4297396827A5064548129 @default.
- W4297396827 hasAuthorship W4297396827A5077410670 @default.
- W4297396827 hasAuthorship W4297396827A5080868374 @default.
- W4297396827 hasAuthorship W4297396827A5083262924 @default.
- W4297396827 hasBestOaLocation W42973968271 @default.
- W4297396827 hasConcept C105569014 @default.
- W4297396827 hasConcept C105795698 @default.
- W4297396827 hasConcept C11413529 @default.
- W4297396827 hasConcept C127313418 @default.
- W4297396827 hasConcept C154945302 @default.
- W4297396827 hasConcept C166693061 @default.
- W4297396827 hasConcept C187320778 @default.
- W4297396827 hasConcept C202444582 @default.
- W4297396827 hasConcept C2524010 @default.
- W4297396827 hasConcept C2780598303 @default.
- W4297396827 hasConcept C33923547 @default.
- W4297396827 hasConcept C38349280 @default.
- W4297396827 hasConcept C41008148 @default.
- W4297396827 hasConcept C459310 @default.
- W4297396827 hasConcept C50644808 @default.
- W4297396827 hasConcept C6648577 @default.
- W4297396827 hasConcept C91188154 @default.
- W4297396827 hasConcept C9652623 @default.
- W4297396827 hasConceptScore W4297396827C105569014 @default.
- W4297396827 hasConceptScore W4297396827C105795698 @default.
- W4297396827 hasConceptScore W4297396827C11413529 @default.
- W4297396827 hasConceptScore W4297396827C127313418 @default.
- W4297396827 hasConceptScore W4297396827C154945302 @default.
- W4297396827 hasConceptScore W4297396827C166693061 @default.
- W4297396827 hasConceptScore W4297396827C187320778 @default.
- W4297396827 hasConceptScore W4297396827C202444582 @default.
- W4297396827 hasConceptScore W4297396827C2524010 @default.
- W4297396827 hasConceptScore W4297396827C2780598303 @default.
- W4297396827 hasConceptScore W4297396827C33923547 @default.
- W4297396827 hasConceptScore W4297396827C38349280 @default.
- W4297396827 hasConceptScore W4297396827C41008148 @default.
- W4297396827 hasConceptScore W4297396827C459310 @default.
- W4297396827 hasConceptScore W4297396827C50644808 @default.
- W4297396827 hasConceptScore W4297396827C6648577 @default.
- W4297396827 hasConceptScore W4297396827C91188154 @default.
- W4297396827 hasConceptScore W4297396827C9652623 @default.
- W4297396827 hasLocation W42973968271 @default.
- W4297396827 hasLocation W42973968272 @default.
- W4297396827 hasOpenAccess W4297396827 @default.
- W4297396827 hasPrimaryLocation W42973968271 @default.
- W4297396827 hasRelatedWork W1995887718 @default.
- W4297396827 hasRelatedWork W2048840464 @default.
- W4297396827 hasRelatedWork W2059861092 @default.
- W4297396827 hasRelatedWork W2088598518 @default.
- W4297396827 hasRelatedWork W2091849826 @default.
- W4297396827 hasRelatedWork W2122402574 @default.
- W4297396827 hasRelatedWork W2131543894 @default.
- W4297396827 hasRelatedWork W2379557245 @default.
- W4297396827 hasRelatedWork W3201524767 @default.
- W4297396827 hasRelatedWork W2773437251 @default.
- W4297396827 isParatext "false" @default.
- W4297396827 isRetracted "false" @default.
- W4297396827 workType "article" @default.