Paper Citation Record · LEDGER

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions

As of 17 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.10201.

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

pith.paper-citation-record.v1
2507.10201 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:42:06.915033Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

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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

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation fbdc31e0-8d7e-49c6-8f01-d59a7ade95c6 · outbound

This paper cites Recent developments combining ensemble smoother and deep gen- erative networks for facies history matching.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Recent developments combining ensemble smoother and deep gen- erative networks for facies history matching

Reference 1

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Observation 748aa60c-0bda-44e5-8a5c-30be2a4cc797 · outbound

This paper cites Integration of en- semble data assimilation and deep learning for history matching facies mod- els.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Integration of en- semble data assimilation and deep learning for history matching facies mod- els

Reference 2

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Observation e96896c0-8f5f-49cb-b634-134ed4c25951 · outbound

This paper cites Geometrical Aspects of Manifold Learning.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Geometrical Aspects of Manifold Learning

Reference 3

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This paper cites Latent space oddity: on the curvature of deep generative models, 2021.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Latent space oddity: on the curvature of deep generative models, 2021

Reference 4

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This paper cites Variational autoencoder or generative adversarial networks? a comparison of two deep learning methods for flow and transport data assimilation.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Variational autoencoder or generative adversarial networks? a comparison of two deep learning methods for flow and transport data assimilation

Reference 5

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Observation 3710eab1-0af3-4638-97ef-de5cf7360fc4 · outbound

This paper cites Topology and data.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Topology and data

Reference 6

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Observation d326cb0e-890f-4f5e-87f5-cf5b9ebfcaa2 · outbound

This paper cites Elsheikh.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Elsheikh

Reference 7

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This paper cites Elsheikh.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Elsheikh

Reference 8

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Observation e72bf6ad-4258-4b6b-a7f2-d7f48ba5fe02 · outbound

This paper cites Elsheikh.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Elsheikh

Reference 9

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Observation e472b293-bd30-409c-8ca1-424dc146d3f8 · outbound

This paper cites Automated discovery of fundamental variables hidden in experimental data.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Automated discovery of fundamental variables hidden in experimental data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:07.134799Z

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Observation 87b025f4-d033-4145-a424-53164e445ab2 · outbound

This paper cites Uncertainty quantification in reservoir prediction: Part 2—handling uncer- tainty in the geological scenario.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Uncertainty quantification in reservoir prediction: Part 2—handling uncer- tainty in the geological scenario

Reference 11

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Observation 9dc7627f-831f-4ed6-ae3d-6a37939403a8 · outbound

This paper cites Generating realistic geology conditioned on physical measurements with generative adversarial networks, 2018.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Generating realistic geology conditioned on physical measurements with generative adversarial networks, 2018

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:07.118869Z

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Observation c2a39cf4-7b44-4e7b-b9e5-94676fbaf3a4 · outbound

This paper cites Edelsbrunner, D.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Edelsbrunner, D

Reference 13

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Observation 0efda595-b18a-489d-b65f-2508d5dd7211 · outbound

This paper cites Guss and Ruslan Salakhutdinov.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Guss and Ruslan Salakhutdinov

Reference 14

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Observation fc600952-14aa-4489-a139-7e885832f8be · outbound

This paper cites The cma evolution strategy: A tutorial, 2023.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions The cma evolution strategy: A tutorial, 2023

Reference 15

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This paper cites Analysis of a complex of statistical variables into principal components.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Analysis of a complex of statistical variables into principal components

Reference 16

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Observation 0dee05d4-b9fa-4333-92a3-69e2c46b8c5d · outbound

This paper cites Deep convolutional autoencoders for robust flow model calibration under uncertainty in geologic continuity.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Deep convolutional autoencoders for robust flow model calibration under uncertainty in geologic continuity

Reference 17

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Observation c74e6b73-7a0b-4d87-b1ec-dd12946a97f1 · outbound

This paper cites Training image-based scenario modeling of fractured reservoirs for flow uncertainty quantification.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Training image-based scenario modeling of fractured reservoirs for flow uncertainty quantification

Reference 18

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Observation bf8cf27e-4d8a-43a0-be7c-9f7dbe4374d8 · outbound

This paper cites Introduction to pullback metric.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Introduction to pullback metric

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:07.069306Z

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Observation 42a60e99-8df8-44a4-8020-60d8ed717ed4 · outbound

This paper cites Training- image based geostatistical inversion using a spatial generative adversarial neural network.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Training- image based geostatistical inversion using a spatial generative adversarial neural network

Reference 20

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Observation 78fa72b7-d5cf-427c-a450-0fe33ba48053 · outbound

This paper cites Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Reference 21

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Observation baaeb2ac-22ab-4ef1-830c-789be2ff27d3 · outbound

This paper cites Lawrence.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Lawrence

Reference 22

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Observation 5906e5a8-8841-493e-b3f0-c33e111177d5 · outbound

This paper cites Feature extraction using a deep learning algorithm for uncertainty quantification of channelized reservoirs.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Feature extraction using a deep learning algorithm for uncertainty quantification of channelized reservoirs

Reference 23

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Observation fa933a4a-16f6-40e5-94e8-0e9a34b734e8 · outbound

This paper cites Maximum likelihood estimation of in- trinsic dimension.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Maximum likelihood estimation of in- trinsic dimension

Reference 24

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Observation 3583c004-583c-425b-ad49-90564957ef8a · outbound

This paper cites Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

Reference 25

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History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Unresolved cited work

Reference 26

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History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Unresolved cited work

Reference 27

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Observation 7b535cae-23cf-42df-ae39-43ffa2028a17 · outbound

This paper cites Rubenstein, Bernhard Schoelkopf, and Ilya Tolstikhin.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Rubenstein, Bernhard Schoelkopf, and Ilya Tolstikhin

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:07.004539Z

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Observation ed2564a5-3ab1-49df-92ff-65cba3d0a769 · outbound

This paper cites Thomas Fletcher.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Thomas Fletcher

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:06.996999Z

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Observation 10b1f358-c442-4add-85ae-40f4341af7f6 · outbound

This paper cites Emerick, and Marco Aur´ elio C.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Emerick, and Marco Aur´ elio C

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:06.989539Z

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Observation bcde64e6-1a39-4210-870b-84a043dfd20b · outbound

This paper cites Geological realism in fluvial facies modelling with gan under variable depositional conditions.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Geological realism in fluvial facies modelling with gan under variable depositional conditions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:06.981978Z

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Observation f9ad1bf8-e808-4e49-bb4b-0f51b4185826 · outbound

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History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:42:06.974253Z

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Observation b464ff68-2c3b-4fe9-84f0-b499cb13ce92 · outbound

This paper cites Wasserstein auto-encoders.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Wasserstein auto-encoders

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:06.965213Z

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Observation 4fff279d-42fd-4bba-8e98-4246ef1b1a58 · outbound

This paper cites Lawrence.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Lawrence

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:06.956343Z

Source-reported events for the cited work

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Observation fa4ece89-45c9-4a7d-8a78-f4a9fccf5992 · outbound

This paper cites Visualizing data using t-sne.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions Visualizing data using t-sne

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:06.947177Z

Source-reported events for the cited work

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Observation e7fca3c0-ebfa-47ab-9ce3-627f50966148 · outbound

This paper cites The choice of the loss function for solving the problem of history matching of the geological and simulation model: thesis for the degree of candidate of technical sciences: spec.

History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions The choice of the loss function for solving the problem of history matching of the geological and simulation model: thesis for the degree of candidate of technical sciences: spec

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:42:06.939220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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