Paper Citation Record · LEDGER

Discovering Governing Equations in the Presence of Uncertainty

As of 13 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2507.09740.

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pith.paper-citation-record.v1
2507.09740 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:55:54.334734Z

measured 37 of 37 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.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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verified fuzzy27
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External citation measurements

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Outbound references

Observation 9d036a96-d2c0-4d8c-b5c1-843a3917e802 · outbound

This paper cites an unresolved cited work.

Discovering Governing Equations in the Presence of Uncertainty Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:55:58.062136Z

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Observation 9a9aa8ad-27ac-49bc-b575-8bb48b6655a0 · outbound

This paper cites Ptolemy and W.

Discovering Governing Equations in the Presence of Uncertainty Ptolemy and W

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:58.047789Z

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Discovering Governing Equations in the Presence of Uncertainty Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:55:58.031956Z

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Observation d8cf1639-b937-4482-acf8-bd813efa30a4 · outbound

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Discovering Governing Equations in the Presence of Uncertainty Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:55:58.011383Z

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Observation b9db3bf9-6ea2-4428-a4e2-6f8dc615b44d · outbound

This paper cites Stationary sequences in hilbert space,.

Discovering Governing Equations in the Presence of Uncertainty Stationary sequences in hilbert space,

Reference 5

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Observation be3655b3-996a-4d67-aa6d-e35776abd049 · outbound

This paper cites Wiener,Extrapolation, Interpolation, and Smoothing of Stationary Time Series.

Discovering Governing Equations in the Presence of Uncertainty Wiener,Extrapolation, Interpolation, and Smoothing of Stationary Time Series

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:57.978874Z

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Observation 88fe5505-6db1-4c57-bbc5-c7aa5451de93 · outbound

This paper cites Automated reverse engineering of nonlinear dynamical systems,.

Discovering Governing Equations in the Presence of Uncertainty Automated reverse engineering of nonlinear dynamical systems,

Reference 7

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Observation 099dae03-966a-4079-9cea-392cf31fbd35 · outbound

This paper cites Distilling free-form natural laws from experimental data,.

Discovering Governing Equations in the Presence of Uncertainty Distilling free-form natural laws from experimental data,

Reference 8

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Observation e0cde17e-0926-49a8-9dc3-6a9366fa6f80 · outbound

This paper cites Nonlinear dynamical system identification from uncertain and indirect measurements,.

Discovering Governing Equations in the Presence of Uncertainty Nonlinear dynamical system identification from uncertain and indirect measurements,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:57.710864Z

Source-reported events for the cited work

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Observation 3869ed7d-ac7d-4607-91c5-7ec91fe36cd3 · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.

Discovering Governing Equations in the Presence of Uncertainty Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T17:55:52.478762Z

Source-reported events for the cited work

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Observation 93e2b60f-dd83-4661-aa84-3cc99413eb1d · outbound

This paper cites Machine learning for fluid mechanics,.

Discovering Governing Equations in the Presence of Uncertainty Machine learning for fluid mechanics,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:57.509487Z

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Observation ef2d456b-b106-4d98-bd8e-f0dd223f0a56 · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.

Discovering Governing Equations in the Presence of Uncertainty Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T17:55:52.580804Z

Source-reported events for the cited work

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Observation a1d1c397-661d-443b-ab2c-fa10efd3c4e6 · outbound

This paper cites A unified sparse optimization framework to learn parsimonious physics-informed models from data,.

Discovering Governing Equations in the Presence of Uncertainty A unified sparse optimization framework to learn parsimonious physics-informed models from data,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:57.310970Z

Source-reported events for the cited work

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Observation 78afa482-b73c-4035-9a69-57166db48217 · outbound

This paper cites Data-driven discovery of coordinates and governing equations,.

Discovering Governing Equations in the Presence of Uncertainty Data-driven discovery of coordinates and governing equations,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T17:55:52.697866Z

Source-reported events for the cited work

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Observation 4f7e7530-4975-4109-924b-a2489827075b · outbound

This paper cites Hidden physics models: Machine learning of nonlinear partial differential equations,.

Discovering Governing Equations in the Presence of Uncertainty Hidden physics models: Machine learning of nonlinear partial differential equations,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:57.108492Z

Source-reported events for the cited work

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Observation 8c06c0c4-7e26-45a3-a322-03fc8f7c0d0b · outbound

This paper cites Hypersindy: Deep generative modeling of nonlinear stochastic governing equations,.

Discovering Governing Equations in the Presence of Uncertainty Hypersindy: Deep generative modeling of nonlinear stochastic governing equations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:56.916940Z

Source-reported events for the cited work

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Observation ed5cc576-e025-4bbf-957d-d67a5436e853 · outbound

This paper cites Generalizing the sindy approach with nested neural networks,.

Discovering Governing Equations in the Presence of Uncertainty Generalizing the sindy approach with nested neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:56.799811Z

Source-reported events for the cited work

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Observation 9772a114-d176-49c3-ac47-ef8b8dfe907c · outbound

This paper cites Sparsifying priors for bayesian uncertainty quantification in model discovery,.

Discovering Governing Equations in the Presence of Uncertainty Sparsifying priors for bayesian uncertainty quantification in model discovery,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:56.595107Z

Source-reported events for the cited work

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Observation 82061f48-d134-4bdf-83d1-97447ff6ba62 · outbound

This paper cites Ensemble-sindy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control,.

Discovering Governing Equations in the Presence of Uncertainty Ensemble-sindy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:56.411489Z

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Observation 32943f7b-6872-4b23-ae52-2a2b49a10a9c · outbound

This paper cites Bayesian identification of dynamical systems,.

Discovering Governing Equations in the Presence of Uncertainty Bayesian identification of dynamical systems,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:56.220662Z

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Observation 330bf868-c123-4b81-97fe-f30dc3241ee5 · outbound

This paper cites BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo.

Discovering Governing Equations in the Presence of Uncertainty BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo

Reference 21

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Observation 92289aa6-d72f-4d45-b32d-aea8b47b9e7f · outbound

This paper cites Impact of time varying interaction: Formation and annihilation of extreme events in dynamical systems,.

Discovering Governing Equations in the Presence of Uncertainty Impact of time varying interaction: Formation and annihilation of extreme events in dynamical systems,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:56.122199Z

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Observation 7b80071b-8254-4cd9-a745-aa0a4279015e · outbound

This paper cites State of the art in directed energy deposition: From additive manufacturing to materials design,.

Discovering Governing Equations in the Presence of Uncertainty State of the art in directed energy deposition: From additive manufacturing to materials design,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:56.000302Z

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Observation dbd0292f-188a-423d-b631-4aa0801c1b26 · outbound

This paper cites Characterization of elastoplastic properties of additively manufactured specimens from indentation data using stochastic inverse mod- eling,.

Discovering Governing Equations in the Presence of Uncertainty Characterization of elastoplastic properties of additively manufactured specimens from indentation data using stochastic inverse mod- eling,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.912091Z

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Observation 1306a15a-2ecd-4aa0-971c-1e84e08fb3d7 · outbound

This paper cites Spatial and temporal variability of soil moisture and its driving factors in the northern agricultural regions of china,.

Discovering Governing Equations in the Presence of Uncertainty Spatial and temporal variability of soil moisture and its driving factors in the northern agricultural regions of china,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.785519Z

Source-reported events for the cited work

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Observation 1d33fdc1-8302-4217-9dd4-bb19d1c01c50 · outbound

This paper cites Combining push-forward measures and bayes’ rule to construct consistent solutions to stochastic inverse problems,.

Discovering Governing Equations in the Presence of Uncertainty Combining push-forward measures and bayes’ rule to construct consistent solutions to stochastic inverse problems,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.678346Z

Source-reported events for the cited work

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Observation 41f88114-b519-449a-ae0a-7cf5a4be3002 · outbound

This paper cites Bayesian variable selection in linear regression,.

Discovering Governing Equations in the Presence of Uncertainty Bayesian variable selection in linear regression,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.562792Z

Source-reported events for the cited work

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Observation d91e58e3-fe98-4096-a1d0-e8ff9ac1522f · outbound

This paper cites Handling sparsity via the horseshoe,.

Discovering Governing Equations in the Presence of Uncertainty Handling sparsity via the horseshoe,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.465097Z

Source-reported events for the cited work

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Observation b33529ff-45ba-4a71-9980-34448295789b · outbound

This paper cites Matched-block bootstrap for dependent data,.

Discovering Governing Equations in the Presence of Uncertainty Matched-block bootstrap for dependent data,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.357916Z

Source-reported events for the cited work

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Observation ea7b22ba-446c-4e9b-a119-3314bcb3f918 · outbound

This paper cites Sindy-sa framework: enhancing nonlinear system identification with symmetry and conservation laws,.

Discovering Governing Equations in the Presence of Uncertainty Sindy-sa framework: enhancing nonlinear system identification with symmetry and conservation laws,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.259896Z

Source-reported events for the cited work

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Observation 348fba12-8113-4813-bdad-35b289981a98 · outbound

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Discovering Governing Equations in the Presence of Uncertainty Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:55:53.910395Z

Source-reported events for the cited work

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Observation 48290296-0859-4e8c-9838-4b67f4277f88 · outbound

This paper cites Variable selection via gibbs sampling,.

Discovering Governing Equations in the Presence of Uncertainty Variable selection via gibbs sampling,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.147037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 113caa00-71e9-4f0a-90e0-691f999e1d63 · outbound

This paper cites Sparsity information and regularization in the horseshoe and other shrinkage priors,.

Discovering Governing Equations in the Presence of Uncertainty Sparsity information and regularization in the horseshoe and other shrinkage priors,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:55.036532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 20fa747a-55b2-4e94-8ba4-3ffc2ffb2f15 · outbound

This paper cites Data-consistent inversion for stochastic input-to- output maps,.

Discovering Governing Equations in the Presence of Uncertainty Data-consistent inversion for stochastic input-to- output maps,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:54.915453Z

Source-reported events for the cited work

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Observation f393897f-e55e-4b6c-acd8-2f8ceb6d2646 · outbound

This paper cites an unresolved cited work.

Discovering Governing Equations in the Presence of Uncertainty Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:55:54.804404Z

Source-reported events for the cited work

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Observation b0eea341-bae8-4cec-b369-eb36575a1e50 · outbound

This paper cites Multimodal boiling dataset with synchronized acoustic, optical, and thermal measurements under steady-state and transient heat loads,.

Discovering Governing Equations in the Presence of Uncertainty Multimodal boiling dataset with synchronized acoustic, optical, and thermal measurements under steady-state and transient heat loads,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:54.701497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 825623b7-5b0f-461a-b731-e0bbbed791b1 · outbound

This paper cites Inertial capillarity,.

Discovering Governing Equations in the Presence of Uncertainty Inertial capillarity,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:55:54.539785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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