Paper Citation Record · LEDGER

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:1908.05823.

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

pith.paper-citation-record.v1
1908.05823 v1

Coverage vector

measured 47 of 47 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 9eba81a5-8d2e-4b01-a060-54bb6023c3ea · outbound

This paper cites an unresolved cited work.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 1

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This paper cites an unresolved cited work.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 2

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This paper cites an unresolved cited work.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 3

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 4

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 6

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 7

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This paper cites Hamdi, I.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Hamdi, I

Reference 8

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This paper cites Bazargan, M.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Bazargan, M

Reference 9

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 10

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Observation bf6f356d-2846-40b0-a177-c7e45aa3649a · outbound

This paper cites Baltrusaitis, P.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Baltrusaitis, P

Reference 11

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 12

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This paper cites Isola, J.-Y.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Isola, J.-Y

Reference 13

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Observation 88af1760-4ce5-4b2a-8946-28c2da968fab · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 14

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 15

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This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 16

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This paper cites Stronger generalization bounds for deep nets via a compression approach.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Stronger generalization bounds for deep nets via a compression approach

Reference 17

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 18

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Observation e169a23f-358a-4df8-82f4-7a41e3f110cb · outbound

This paper cites Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

Reference 19

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This paper cites Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification

Reference 20

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This paper cites Deep-learning-based reduced-order modeling for subsurface flow simulation.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Deep-learning-based reduced-order modeling for subsurface flow simulation

Reference 21

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This paper cites Ronneberger, P.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Ronneberger, P

Reference 22

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This paper cites Hochreiter, J.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Hochreiter, J

Reference 23

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Xingjian, Z

Reference 24

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

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This paper cites A guide to convolution arithmetic for deep learning.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems A guide to convolution arithmetic for deep learning

Reference 27

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Mikolov, M

Reference 30

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Bengio, P

Reference 31

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 32

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 33

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This paper cites Hecht-Nielsen, Theory of the backpropagation neural network, in: Neural Networks for Perception, Elsevier, 65–93, 1992.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Hecht-Nielsen, Theory of the backpropagation neural network, in: Neural Networks for Perception, Elsevier, 65–93, 1992

Reference 34

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Adam: A Method for Stochastic Optimization

Reference 35

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This paper cites Measuring the Intrinsic Dimension of Objective Landscapes.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Measuring the Intrinsic Dimension of Objective Landscapes

Reference 36

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This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 37

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

Reference 38

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

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This paper cites Zhou, Parallel general-purpose reservoir simulation with coupled reservoir models and multisegment wells, Ph.D.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Zhou, Parallel general-purpose reservoir simulation with coupled reservoir models and multisegment wells, Ph.D

Reference 41

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Bergstra, Y

Reference 42

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

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A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Unresolved cited work

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This paper cites Audet, J.

A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems Audet, J

Reference 47

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Pith citing papers

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