Paper Citation Record · LEDGER

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements

As of 8 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2508.05908.

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

pith.paper-citation-record.v1
2508.05908 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:12:24.047898Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-03T06:16:11.147974Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation 1cf24bd0-a899-4320-a0b0-4fcea6e09dd5 · outbound

This paper cites write newline.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

Reference 1

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Observation 94a9ac92-bfce-4f16-8ea1-06e54710604d · outbound

This paper cites sn-aps.bst.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements sn-aps.bst

Reference 2

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Observation a33d3b9c-b83a-4e82-a99a-39bf21fe45e9 · outbound

This paper cites write newline.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

Reference 3

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Observation 4bdfe452-dcf4-4832-b5f5-47a045fcea3a · outbound

This paper cites , Bengio , Y.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Bengio , Y

Reference 4

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Observation 16219189-2e2c-4cbf-ac15-1f309ba60102 · outbound

This paper cites , Sutskever , I.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Sutskever , I

Reference 5

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Observation 808010a6-60ed-488f-8adc-6f16b6a3e4cc · outbound

This paper cites , Zhang , X.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Zhang , X

Reference 6

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Observation 562758e3-95f4-4845-be47-56ffaeeb86ab · outbound

This paper cites Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Reference 7

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Observation 0e4ba584-c272-4d67-8b23-0b2db694a868 · outbound

This paper cites , Antonoglou , I.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Antonoglou , I

Reference 8

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Observation becf4d12-a8b1-496b-a679-7a1d15571fc9 · outbound

This paper cites , Stinchcombe , M.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Stinchcombe , M

Reference 9

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Observation 9e25cd08-e2fb-4ae4-8542-9316c5fa41e6 · outbound

This paper cites , Smith , K.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Smith , K

Reference 10

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Observation a2eb2bce-43b4-4fe6-842e-09c55bdcfc32 · outbound

This paper cites Physical Design using Differentiable Learned Simulators.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements Physical Design using Differentiable Learned Simulators

Reference 11

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Observation 3c5e53c4-e4c4-456d-b5fe-049d78052186 · outbound

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

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements Learning Mesh-Based Simulation with Graph Networks

Reference 12

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Observation b746e3db-adc1-4aeb-9003-79464bc50c12 · outbound

This paper cites , Szunyogh , I.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Szunyogh , I

Reference 13

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Observation d12acc35-5799-43d6-b91d-5b4c2cf07a95 · outbound

This paper cites , Caro , M.A.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Caro , M.A

Reference 14

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Observation 66b33466-978d-4653-88b6-02ee174992c6 · outbound

This paper cites , Cubuk , E.D.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Cubuk , E.D

Reference 15

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Observation 3db7eb97-28b5-4ce4-af3d-8c032752884a · outbound

This paper cites , Pearlmutter , B.A.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Pearlmutter , B.A

Reference 16

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Observation 1c7fed69-3062-4e7a-ab81-c404f749c254 · outbound

This paper cites , Broek , W.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Broek , W

Reference 17

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Observation 25ab271a-3fc3-43cf-aa8a-0bced2cfbf14 · outbound

This paper cites , Koch , C.T.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Koch , C.T

Reference 18

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Observation 18acc7f2-a0e0-4d20-8d22-95a4984c5052 · outbound

This paper cites , Herdegen , Z.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Herdegen , Z

Reference 19

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Observation 0ef093cc-8333-4eb5-aeec-db0a78359e22 · outbound

This paper cites : The scattering of fast electrons by crystals.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements : The scattering of fast electrons by crystals

Reference 20

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Observation 0bb830a7-47d7-4e08-9fd7-f0f83caada24 · outbound

This paper cites : Advanced Computing in Electron Microscopy.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements : Advanced Computing in Electron Microscopy

Reference 21

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This paper cites , Brázda , P.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Brázda , P

Reference 22

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Observation dfc9e0c5-b5e5-4930-87e1-dc516d53b94c · outbound

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements : The potential and limitations of neutrons, electrons and x-rays for atomic resolution microscopy of unstained biological molecules

Reference 23

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Observation b1be26cd-8904-44a6-b8ab-88d9ddbc7d45 · outbound

This paper cites , Shi , D.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Shi , D

Reference 24

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Observation a38319c1-6795-4031-90d4-f99708a9d0d4 · outbound

This paper cites , Moodie , A.F.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Moodie , A.F

Reference 25

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Observation 8f00dd6b-f640-4f37-a131-92e3353cbfa8 · outbound

This paper cites , Palatinus , L.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Palatinus , L

Reference 26

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Observation 2b0a0c24-60c8-49b4-a49f-1cd7ef958605 · outbound

This paper cites , Mugnaioli , E.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Mugnaioli , E

Reference 27

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Observation 9eec0430-796c-492b-a9ed-0716522e366c · outbound

This paper cites , Yörük , E.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Yörük , E

Reference 28

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This paper cites : Zur theorie des durchgangs schneller korpuskularstrahlen durch materie.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements : Zur theorie des durchgangs schneller korpuskularstrahlen durch materie

Reference 29

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Petříček , V

Reference 30

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Observation ae1d7d67-1561-43ce-bca4-91fb6077fda8 · outbound

This paper cites , Spence , J.C.H.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Spence , J.C.H

Reference 31

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This paper cites , Hinton , G.E.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Hinton , G.E

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Brázda , P

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Jacob , D

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This paper cites , Krysiak , Y.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Krysiak , Y

Reference 35

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements Adam: A Method for Stochastic Optimization

Reference 36

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements http://pets.fzu.cz/ Accessed 2025-05-06 botherref

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Observation a0623c54-82a4-4168-9b08-f88e98017c62 · outbound

This paper cites : Modelling dynamical 3D electron diffraction intensities.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements : Modelling dynamical 3D electron diffraction intensities

Reference 38

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements Phase Scrambling

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Gross , S

Reference 40

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Observation 3a5f8ada-1de4-4543-82cb-9ce2207f38e1 · outbound

This paper cites , Howie , A.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Howie , A

Reference 41

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Observation 0b3b9696-ed9c-452b-8e9d-ecbd693be7a3 · outbound

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Zuo , J.-M

Reference 42

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Observation e5d676ac-f0e6-44c4-b7e6-fcec97c129dd · outbound

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Adams , P.D

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Observation 5d5064d6-88b2-4ce0-b508-f8bc706ad321 · outbound

This paper cites Auto-Encoding Variational Bayes.

Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements Auto-Encoding Variational Bayes

Reference 44

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Gommers , R

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements sn-basic.bst

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements sn-nature.bst

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements , Nocedal , J

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements sn-vancouver-num.bst

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Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements write newline

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Pith citing papers

Observation 179f88b5-51af-48a9-912d-e718106c0744 · inbound

The role of absorption in three-dimensional electron diffraction dynamical structure refinement cites this paper.

The role of absorption in three-dimensional electron diffraction dynamical structure refinement Hybrid Physics-Machine Learning Models for Quantitative Electron Diffraction Refinements

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.