Paper Citation Record · LEDGER

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems

As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2608.01582.

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

pith.paper-citation-record.v1
2608.01582 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T00:46:52.827364Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact1
verified fuzzy27
unresolved10
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 43a9a705-c9a2-4bf6-9a0b-2c8f1f2d3468 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , series=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Proceedings of the 41st International Conference on Machine Learning , series=

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.321514Z

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.

Observation 559182f1-737c-4c9d-b613-595f4c5f5fdb · outbound

This paper cites Machine Learning: Science and Technology , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Machine Learning: Science and Technology , volume=

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.312527Z

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.

Observation 58c3266e-318b-4a3f-bb2e-25b3aa2ede49 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in Neural Information Processing Systems , volume=

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.302135Z

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.

Observation ba4d7c93-3c51-4737-9f48-5a25e9605c62 · outbound

This paper cites Advances in neural information processing systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in neural information processing systems , volume=

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:51.051268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 820d0b5e-5af6-4f5e-bdde-34a8551b7710 · outbound

This paper cites Physical Review Letters , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Physical Review Letters , volume=

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.287088Z

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.

Observation d0069fdb-5ec8-4f7d-aa8e-aacf7624be22 · outbound

This paper cites Proceedings of the 42nd International Conference on Machine Learning , series=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Proceedings of the 42nd International Conference on Machine Learning , series=

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.277636Z

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.

Observation de164363-f484-49cd-bd9b-0fb8b690cdb2 · outbound

This paper cites Physics Today , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Physics Today , volume=

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.269095Z

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.

Observation c3348c7e-fe2b-405a-b452-f170525c10a5 · outbound

This paper cites I , author=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems I , author=

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.260101Z

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.

Observation 67147b95-be10-46ae-a28f-a43c717b21f2 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in Neural Information Processing Systems , volume=

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.250627Z

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.

Observation 1c9a9ab8-0f83-4da7-b8f5-6408892ba1a4 · outbound

This paper cites International conference on machine learning , pages=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems International conference on machine learning , pages=

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:51.400564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0497ef4f-3822-4c55-8c56-0f4ee214f720 · outbound

This paper cites Physical Review Letters , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Physical Review Letters , volume=

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.235854Z

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.

Observation ba5f1a4a-1043-4a6e-9f9f-bd19c1838785 · outbound

This paper cites International conference on machine learning , pages=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems International conference on machine learning , pages=

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:51.537624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e858aa3-aa24-4c45-920a-20050c65173a · outbound

This paper cites Physical Review D , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Physical Review D , volume=

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.221346Z

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.

Observation fef9f8c0-3082-4f16-b29e-44d4d1bddd9a · outbound

This paper cites Advances in neural information processing systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in neural information processing systems , volume=

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:51.629687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e14acc06-c9d6-42ed-a765-ca2da956aa5e · outbound

This paper cites Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:51.709067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd914341-a031-4851-abda-935a4340f3c0 · outbound

This paper cites 1993 , publisher=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems 1993 , publisher=

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.206600Z

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.

Observation ac234850-aa08-4f65-b76c-ef9b036f1446 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in Neural Information Processing Systems , volume=

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.198412Z

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.

Observation 7a3fd088-6031-4a37-ade0-104641ae7e28 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in Neural Information Processing Systems , volume=

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:51.880629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fa7f1ff-986f-472c-8d29-4d1620707779 · outbound

This paper cites Advances in neural information processing systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in neural information processing systems , volume=

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.184756Z

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.

Observation 6da90730-b038-4144-88ed-ae58b54f3089 · outbound

This paper cites Advances in neural information processing systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in neural information processing systems , volume=

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:52.065717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4528d0ae-b565-4c40-8d58-749c740474c2 · outbound

This paper cites Incorporating Symmetry into Deep Dynamics Models for Improved Generalization.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:52.147626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab4d78de-0dc7-4a59-81d5-98c69ba97d33 · outbound

This paper cites Physics-Guided Deep Learning for Dynamical Systems: A Survey.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Physics-Guided Deep Learning for Dynamical Systems: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:52.241766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a4be0a9-3172-4804-a633-ddb0bc9d6d5f · outbound

This paper cites Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T00:46:52.271683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48740e1e-f3d2-4b78-8b1b-25918c55fe66 · outbound

This paper cites Proceedings of the 42nd International Conference on Machine Learning , series=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Proceedings of the 42nd International Conference on Machine Learning , series=

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.170248Z

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.

Observation bec81cc7-12d0-4ddb-baa0-9715a6cd1b32 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Advances in Neural Information Processing Systems , volume=

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.161404Z

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.

Observation d4f04a58-fa05-44bc-95e6-638a391b4eac · outbound

This paper cites Proceedings of the 40th International Conference on Machine Learning , series=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Proceedings of the 40th International Conference on Machine Learning , series=

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.152921Z

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.

Observation b529cda8-7907-42ba-96a3-e3df6a679a62 · outbound

This paper cites Physical Review E , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Physical Review E , volume=

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.144254Z

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.

Observation e720397e-431c-479d-875f-f8eed656e8bb · outbound

This paper cites Proceedings of the 42nd International Conference on Machine Learning , series=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Proceedings of the 42nd International Conference on Machine Learning , series=

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.135486Z

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.

Observation 6e84e4b3-9dd5-4647-b8b5-d4c55a58cd40 · outbound

This paper cites Machine Learning: Science and Technology , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Machine Learning: Science and Technology , volume=

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.126932Z

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.

Observation 91aee405-1f23-419e-bafc-8f3a7807e5b4 · outbound

This paper cites arXiv preprint arXiv:2505.08219 , year=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems arXiv preprint arXiv:2505.08219 , year=

Reference 30

Resolution: verified exact; raw_fallback, observed 2026-08-05T00:46:53.007601Z

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.

Observation f7b4539f-3d67-4e6f-8f1b-bba496c470c2 · outbound

This paper cites Journal of Physics A: Mathematical and General , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Journal of Physics A: Mathematical and General , volume=

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.118621Z

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.

Observation 152efb50-bdfb-4966-860f-bb8f718d5203 · outbound

This paper cites Journal of Mathematical Physics , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Journal of Mathematical Physics , volume=

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.109732Z

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.

Observation e0ed8d2d-6942-4265-80fd-a57dc2510c7f · outbound

This paper cites Machine Learning: Science and Technology , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Machine Learning: Science and Technology , volume=

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.101037Z

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.

Observation eef73f1e-d700-4cfc-b823-224a5b802447 · outbound

This paper cites Proceedings of the 39th International Conference on Machine Learning (ICML) , series=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Proceedings of the 39th International Conference on Machine Learning (ICML) , series=

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.091196Z

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.

Observation c2d6b44a-0021-4d6e-ac0b-a8e4e9571be3 · outbound

This paper cites Journal of Physics A: Mathematical and Theoretical , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Journal of Physics A: Mathematical and Theoretical , volume=

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.082760Z

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.

Observation 4b816b6f-a83e-4c7a-a6e7-233eb58ecc4c · outbound

This paper cites Journal of Physics A: Mathematical and Theoretical , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Journal of Physics A: Mathematical and Theoretical , volume=

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.074137Z

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.

Observation 0699d2e1-a6c0-483b-9cb8-38ae1a962c87 · outbound

This paper cites Journal of Physics A: Mathematical and Theoretical , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Journal of Physics A: Mathematical and Theoretical , volume=

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.064232Z

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.

Observation 0895717d-f036-43f0-a0b4-8edd476c27b0 · outbound

This paper cites Journal of Mathematical Physics , volume=.

LieStoNet: Learning Lie Symmetries from Spatiotemporal Data for Stochastic Dynamical Systems Journal of Mathematical Physics , volume=

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:46:53.054552Z

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.

Pith citing papers

No inbound Pith citation observations are available.