Paper Citation Record · LEDGER

Discovering Interpretable Ordinary Differential Equations from Noisy Data

As of 7 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2507.21841.

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

pith.paper-citation-record.v1
2507.21841 v1

Coverage vector

measured 26 of 26 reference resolution

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measured 26 of 26 standing notices

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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

26 of 26 outbound references displayed

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External citation measurements

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Outbound references

Observation e7600326-59f1-4e8a-b160-04aad56416a3 · outbound

This paper cites Learning nonparametric ordinary differential equations from noisy data.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Learning nonparametric ordinary differential equations from noisy data

Reference 1

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Observation 9f54d0cf-c3b8-42a8-9cbc-12fe15118bdd · outbound

This paper cites Determining the ordinary differential equation from noisy data.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Determining the ordinary differential equation from noisy data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.843013Z

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Observation 6584efc4-1f61-4535-a2f8-12af5816d2dc · outbound

This paper cites Practical approximate solutions to linear operator equations when the data are noisy.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Practical approximate solutions to linear operator equations when the data are noisy

Reference 3

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Observation 84821621-c368-4968-b60b-23bb74c4503e · outbound

This paper cites Equation discovery with bayesian spike-and-slab priors and efficient kernels.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Equation discovery with bayesian spike-and-slab priors and efficient kernels

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.792139Z

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Observation 0fbdf348-a89a-4abb-82ae-1982d35d4728 · outbound

This paper cites Bayesian spline learning for equation discovery of nonlinear dynamics with quantified uncertainty.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Bayesian spline learning for equation discovery of nonlinear dynamics with quantified uncertainty

Reference 5

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Observation c1b9ffeb-1c3c-41aa-86d0-dfbb5f65d74c · outbound

This paper cites Distilling free-form natural laws from experimental data.science, 324(5923):81– 85, 2009.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Distilling free-form natural laws from experimental data.science, 324(5923):81– 85, 2009

Reference 6

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Observation f74b02ab-ad0b-459f-bdf0-812692ad0831 · outbound

This paper cites Genetic algorithm.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Genetic algorithm

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.726657Z

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Observation 66799147-8ac8-486d-a81b-7ab92e7c00a6 · outbound

This paper cites The alamo approach to machine learning.

Discovering Interpretable Ordinary Differential Equations from Noisy Data The alamo approach to machine learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.702150Z

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Observation eb7492f9-7c38-4c58-b490-9221d70b55c9 · outbound

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

Discovering Interpretable Ordinary Differential Equations from Noisy Data Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 9

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Observation 2f9889cb-702b-44b5-ac7f-bf7fc5bc4f1d · outbound

This paper cites Derivative-based sindy (dsindy): Addressing the challenge of discovering governing equations from noisy data.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Derivative-based sindy (dsindy): Addressing the challenge of discovering governing equations from noisy data

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.652141Z

Source-reported events for the cited work

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Observation 56b6dabc-348d-4480-9988-05c0fb873902 · outbound

This paper cites Data-driven discovery of the governing equations of dynamical systems via moving horizon optimization.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Data-driven discovery of the governing equations of dynamical systems via moving horizon optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.627143Z

Source-reported events for the cited work

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Observation c636b3b3-aba4-4075-aa35-07ff6c00aa58 · outbound

This paper cites Compressive-sensing-assisted mixed integer optimization for dynamical system discovery with highly noisy data.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Compressive-sensing-assisted mixed integer optimization for dynamical system discovery with highly noisy data

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.602193Z

Source-reported events for the cited work

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Observation eaf2c110-65f5-4a7e-ac8d-c8c89bcb938b · outbound

This paper cites Ai feynman: A physics-inspired method for symbolic regression.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Ai feynman: A physics-inspired method for symbolic regression

Reference 13

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Observation 74e6aff1-3919-45ce-8f75-41260d716440 · outbound

This paper cites Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.567046Z

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Observation ec5476d3-86a6-405e-a4d5-c3812beac17c · outbound

This paper cites Hamiltonian neural networks.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Hamiltonian neural networks

Reference 15

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Observation 5a623df9-4330-40ac-a755-12ff3d79c87b · outbound

This paper cites Lagrangian Neural Networks.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Lagrangian Neural Networks

Reference 16

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Observation 44c4a582-5ad0-4323-aa00-0980619f7c66 · outbound

This paper cites Wronskian determinants and the zeros of certain functions.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Wronskian determinants and the zeros of certain functions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.515865Z

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Observation a55d418b-b896-4b8a-a8cc-ca79224b9f5b · outbound

This paper cites Eshelman and J.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Eshelman and J

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.491139Z

Source-reported events for the cited work

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Observation b5796f9f-41f0-47c0-a39f-ba67ecb7d2cf · outbound

This paper cites An efficient constraint handling method for genetic algorithms.

Discovering Interpretable Ordinary Differential Equations from Noisy Data An efficient constraint handling method for genetic algorithms

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.467455Z

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Observation 6c095f1f-30f6-4bf5-bb59-a00027bdeaa2 · outbound

This paper cites Goldberg.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Goldberg

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.441624Z

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Observation 9f822f96-461d-4a70-81c4-4a0c5bb2ee1e · outbound

This paper cites Trefethen and D.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Trefethen and D

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.410965Z

Source-reported events for the cited work

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Observation 7405e20e-a671-41e7-96d6-835f8a0576cb · outbound

This paper cites Inverse eigenvalue problems associated with spring-mass systems.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Inverse eigenvalue problems associated with spring-mass systems

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:29:22.391081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94fb01ea-b2f3-4172-83d6-c59bec7fe135 · outbound

This paper cites Discovering governing equation in structural dynamics from acceleration-only measurements.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Discovering governing equation in structural dynamics from acceleration-only measurements

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:29:22.298540Z

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Observation 970cafa4-3559-4ea9-b429-8464f24a35c3 · outbound

This paper cites PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data.

Discovering Interpretable Ordinary Differential Equations from Noisy Data PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T12:29:22.193103Z

Source-reported events for the cited work

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Observation 0cc4b7e4-7940-433b-8623-29f647efc659 · outbound

This paper cites Weak sindy for partial differential equations.

Discovering Interpretable Ordinary Differential Equations from Noisy Data Weak sindy for partial differential equations

Reference 25

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Source-reported events for the cited work

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Observation 83a01ef2-dfa6-45d4-97e2-b85b3e846c47 · outbound

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Discovering Interpretable Ordinary Differential Equations from Noisy Data Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:29:22.349957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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