Paper Citation Record · LEDGER

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks

As of 17 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2505.05869.

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

pith.paper-citation-record.v1
2505.05869 v1

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measured 50 of 50 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 7fc5765c-365f-4f0d-856f-e0fe5716df93 · outbound

This paper cites an unresolved cited work.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:59:53.752508Z

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Observation 61d235a4-5a60-49d7-a75e-b6a884cb91e6 · outbound

This paper cites & Bolton, T.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Bolton, T

Reference 2

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Observation 5fd3ce86-524d-4ad9-8fec-27f75f5eb8b5 · outbound

This paper cites an unresolved cited work.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 3

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 4

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Observation 41414072-5824-4d97-beb2-b1ef72856641 · outbound

This paper cites L., Proctor, J.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks L., Proctor, J

Reference 5

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Observation 5420f59d-b983-44c9-acf8-ea5ad5c8f919 · outbound

This paper cites H., Brunton, S.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks H., Brunton, S

Reference 6

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Observation 1d1ce6e4-2692-400d-9c6a-370a1cb0f2aa · outbound

This paper cites & Earls, C.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Earls, C

Reference 7

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Observation f57f00a1-b415-4034-8a50-c751fd51f102 · outbound

This paper cites an unresolved cited work.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 8

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Observation 46b4ee3d-9866-493f-beff-0c84b9ddddb5 · outbound

This paper cites & Kang, S.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Kang, S

Reference 9

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Observation f8a3c6d1-306a-45f5-ab64-742ec00907f9 · outbound

This paper cites & Zhang, D.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Zhang, D

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.624050Z

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Observation 201e2b11-509d-4fc8-a92f-8236a96cf6a9 · outbound

This paper cites & Zhang, D.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Zhang, D

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.610157Z

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Observation 6351876a-fc2d-4937-a7b0-9aec5e413a51 · outbound

This paper cites & Zhang, D.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Zhang, D

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.594812Z

Source-reported events for the cited work

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Observation 0562bba5-c00d-4eb8-a468-0b2dcfa1d271 · outbound

This paper cites Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search

Reference 13

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Virgolin, M

Reference 14

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Observation 6d8138c4-c5a4-43b1-adf4-c04eab9437a1 · outbound

This paper cites & Chawla, S.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Chawla, S

Reference 15

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Observation 923d9a07-9758-44ae-ba2b-803d3a4905ad · outbound

This paper cites Attention is all you need.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Attention is all you need

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.539970Z

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Observation e62b8cad-9eb8-4c97-8b92-4c4f5e222a16 · outbound

This paper cites & Zhang, D.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Zhang, D

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.521449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d276437-16a1-4af4-b874-be2d81c9b840 · outbound

This paper cites LLM-SR: Scientific Equation Discovery via Programming with Large Language Models.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

Reference 18

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Observation 4ee51e6c-e86d-4a44-8f3b-7c5a8e068876 · outbound

This paper cites ChatSR: Multimodal Large Language Models for Scientific Formula Discovery.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks ChatSR: Multimodal Large Language Models for Scientific Formula Discovery

Reference 19

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Observation adf68c7d-d4ab-40cc-be4e-d00cbd844bd2 · outbound

This paper cites PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations

Reference 20

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Schaeffer, H

Reference 21

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This paper cites Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning

Reference 22

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:59:53.493529Z

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Observation 0e8964a7-1570-4d73-9037-7a260354836b · outbound

This paper cites an unresolved cited work.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:59:53.480186Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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This paper cites SymbolicGPT: A Generative Transformer Model for Symbolic Regression.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks SymbolicGPT: A Generative Transformer Model for Symbolic Regression

Reference 25

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This paper cites Nonlinear Partial Differential Equations for Scientists and Engineers.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Nonlinear Partial Differential Equations for Scientists and Engineers

Reference 26

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Observation 064b3252-a1e1-437e-830c-0830209cc532 · outbound

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 27

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Observation dbabce15-4e84-4249-a435-876e01480102 · outbound

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Zhang, D

Reference 28

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Earls, C

Reference 29

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Lin, G

Reference 30

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 31

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks N., Brunton, B

Reference 32

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Observation 2c886fb7-9641-4c3a-b6f6-18290c895919 · outbound

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Zhang, D

Reference 33

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Zhang, D

Reference 34

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:59:53.301539Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2f315349-93f7-467a-b6c2-74419168a956 · outbound

This paper cites Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

Reference 36

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 37

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This paper cites The Mathematica Book.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks The Mathematica Book

Reference 38

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 39

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 40

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 41

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks M., Fang, Y

Reference 42

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This paper cites H., Tang, T., Adcock, T.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks H., Tang, T., Adcock, T

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.189247Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c8d4428a-0b91-446c-877e-cd2800ba1d96 · outbound

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:59:53.171287Z

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Observation 487111dd-688e-47b7-be7b-7d3652ae8e08 · outbound

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Enbo, W

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.138641Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e4651c61-f86d-4022-a5d6-ef7d65567e68 · outbound

This paper cites Distilling Wikipedia mathematical knowledge into neural network models.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Distilling Wikipedia mathematical knowledge into neural network models

Reference 46

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Observation 1112fda1-55a2-4c4b-bd1c-4e2920b559c0 · outbound

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Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Osindero, S

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.118097Z

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Observation 7790ec49-1dfc-444e-a44f-bf8bcc813586 · outbound

This paper cites an unresolved cited work.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:59:53.100578Z

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Observation 73a5aea7-2652-40e6-a940-bec3cd1a7e56 · outbound

This paper cites & Karniadakis, G.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks & Karniadakis, G

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:53.083091Z

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Observation 67bd42b7-ee39-4044-8303-dd4eb359a84e · outbound

This paper cites smiley face region.

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks smiley face region

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Pith citing papers

No inbound Pith citation observations are available.