Paper Citation Record · LEDGER

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network

As of 13 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2411.14192.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.14192 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:28:35.426260Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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verified fuzzy16
unresolved6
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6857160-d518-4b9a-b0c2-e46ee503d43e · outbound

This paper cites Masson-Delmotte, P.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Masson-Delmotte, P

Reference 1

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Source-reported events for the cited work

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Observation d3710e0d-3620-4ce3-95c5-2003ee9a29db · outbound

This paper cites Progress in carbon capture technologies.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Progress in carbon capture technologies

Reference 2

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Observation 1df2ea5c-2244-4adb-84aa-4cdd4fd5ff8e · outbound

This paper cites Carbon capture and storage: history and the road ahead.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Carbon capture and storage: history and the road ahead

Reference 3

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Source-reported events for the cited work

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Observation 5ee04db5-bac9-48f5-a0bf-8e291d162f11 · outbound

This paper cites The role of carbon capture and storage to achieve net-zero energy systems: Trade-offs between economics and the environment.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network The role of carbon capture and storage to achieve net-zero energy systems: Trade-offs between economics and the environment

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:36.176241Z

Source-reported events for the cited work

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Observation 8db2cbd6-f5c2-4930-9f43-68c28188deea · outbound

This paper cites A review on underground hydrogen storage: Insight into geological sites, influencing factors and future outlook.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network A review on underground hydrogen storage: Insight into geological sites, influencing factors and future outlook

Reference 5

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Source-reported events for the cited work

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Observation b4b746dd-2902-4511-bb15-fc2319049a03 · outbound

This paper cites A review of cell-scale multiphase flow modeling, including water management, in polymer electrolyte fuel cells.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network A review of cell-scale multiphase flow modeling, including water management, in polymer electrolyte fuel cells

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a02862b-a4f3-4ebe-b167-f880511ca9b8 · outbound

This paper cites Pore-scale imaging and modelling.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Pore-scale imaging and modelling

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.933417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e68ab936-2b19-4afe-9ef8-458bd1b4c533 · outbound

This paper cites Real-time imaging reveals distinct pore-scale dynamics during transient and equilibrium subsurface multiphase flow.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Real-time imaging reveals distinct pore-scale dynamics during transient and equilibrium subsurface multiphase flow

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c9941564-6d10-42e1-a4e8-ce95bca249b5 · outbound

This paper cites Compre- hensive comparison of pore-scale models for multiphase flow in porous media.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Compre- hensive comparison of pore-scale models for multiphase flow in porous media

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.899383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 863532a0-6054-4a86-a3b0-bf605f5694e7 · outbound

This paper cites An intercomparison of the pore network to the navier–stokes modeling approach applied for saturated conductivity estimation from x-ray ct images.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network An intercomparison of the pore network to the navier–stokes modeling approach applied for saturated conductivity estimation from x-ray ct images

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.884914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4f94779b-57e7-4a3c-b59f-978f0e80a631 · outbound

This paper cites Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Review of pore network modelling of porous media: Experimental characterisations, network constructions and applications to reactive transport

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.869391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2814afd6-fcf2-4dfd-a3fd-64148da22257 · outbound

This paper cites Poreflow-net: A 3d convolutional neural network to predict fluid flow through porous media.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Poreflow-net: A 3d convolutional neural network to predict fluid flow through porous media

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.839978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f64bad84-3310-4e95-9968-46ec982114cf · outbound

This paper cites Neural network–based pore flow field prediction in porous media using super resolution.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Neural network–based pore flow field prediction in porous media using super resolution

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.723167Z

Source-reported events for the cited work

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Observation 9dd3a7d8-f54d-4ffb-a73f-bb0cafdc9d9a · outbound

This paper cites Learning Mesh-Based Simulation with Graph Networks.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Learning Mesh-Based Simulation with Graph Networks

Reference 14

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Source-reported events for the cited work

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Observation 6375d6ad-1c77-4790-b1cb-306d5dc47dc7 · outbound

This paper cites Learning to simulate complex physics with graph networks.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Learning to simulate complex physics with graph networks

Reference 15

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Source-reported events for the cited work

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Observation 076cd9cc-6156-4bd5-96a6-f58befded042 · outbound

This paper cites Learning large-scale subsurface simulations with a hybrid graph network simulator.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Learning large-scale subsurface simulations with a hybrid graph network simulator

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.671691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e849fef5-6bd0-4f7d-8a0b-17e0eb08c6e5 · outbound

This paper cites Python workflow for segmenting multiphase flow in porous rocks.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Python workflow for segmenting multiphase flow in porous rocks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.656361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2189579b-2849-4f13-9e66-091908c5cf33 · outbound

This paper cites MultiScale MeshGraphNets.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network MultiScale MeshGraphNets

Reference 18

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Source-reported events for the cited work

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Observation 6106a40f-45ae-4737-91e0-1d7d26034884 · outbound

This paper cites On the optimal combi- nation of cross-entropy and soft dice losses for lesion segmentation with out-of-distribution robustness.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network On the optimal combi- nation of cross-entropy and soft dice losses for lesion segmentation with out-of-distribution robustness

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.639886Z

Source-reported events for the cited work

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Observation e79308cd-b293-4ef1-a847-af99e0e76a4f · outbound

This paper cites Distance Map Loss Penalty Term for Semantic Segmentation.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Distance Map Loss Penalty Term for Semantic Segmentation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T15:28:35.416971Z

Source-reported events for the cited work

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Observation 284a30d3-3e7d-4da4-a6b8-32285b6cd68b · outbound

This paper cites Focal loss for dense object detection.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Focal loss for dense object detection

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T15:28:35.421349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdf774f7-dcc7-42f5-98bf-f0ae77551bfc · outbound

This paper cites Tversky loss function for image segmentation using 3d fully convolutional deep networks.

Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network Tversky loss function for image segmentation using 3d fully convolutional deep networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:28:35.613500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.