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Paper Citation Record · LEDGER

Sub-Sequential Physics-Informed Learning with State Space Model

As of 11 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 5 inbound Pith citation observations for arXiv:2502.00318.

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

pith.paper-citation-record.v1
2502.00318 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:31:28.899565Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:16:00.005795Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T06:06:41.617423Z

Reference resolution

51 of 51 outbound references displayed

  • verified exact0
  • verified fuzzy27
  • unresolved24
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f68a94ff-8f9d-4446-9475-eadb726618a2 · outbound

This paper cites write newline.

Sub-Sequential Physics-Informed Learning with State Space Model write newline

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 08936479-fbe1-47cd-9b93-ef2c4bf1a1a8 · outbound

This paper cites tensorflow : a system for large-scale machine learning.

Sub-Sequential Physics-Informed Learning with State Space Model tensorflow : a system for large-scale machine learning

Reference 2

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 713b46ae-aec5-40d7-aa9b-383288509a6d · outbound

This paper cites and Karpatne, A.

Sub-Sequential Physics-Informed Learning with State Space Model and Karpatne, A

Reference 3

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 65b8e0e2-754e-4e3c-bab2-803dbe3af350 · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 4

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 5c1c1f57-fb23-4b4b-9ac0-852d26ea6548 · outbound

This paper cites Parameterized physics-informed neural networks for parameterized PDE s.

Sub-Sequential Physics-Informed Learning with State Space Model Parameterized physics-informed neural networks for parameterized PDE s

Reference 5

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation b90e19e2-4483-452c-b67e-c4766d3e2c8a · outbound

This paper cites and Gu, A.

Sub-Sequential Physics-Informed Learning with State Space Model and Gu, A

Reference 6

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 26201709-91ec-484e-a5df-e29d3083f38d · outbound

This paper cites Extended tanh-function method and its applications to nonlinear equations.

Sub-Sequential Physics-Informed Learning with State Space Model Extended tanh-function method and its applications to nonlinear equations

Reference 7

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raw_fallback, observed 2026-08-09T19:31:29.530423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation ddcf7a85-9d59-474e-b755-9e16c0ac346f · outbound

This paper cites Y., Dao, T., Saab, K.

Sub-Sequential Physics-Informed Learning with State Space Model Y., Dao, T., Saab, K

Reference 8

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raw_fallback, observed 2026-08-09T19:31:29.515623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 7a457472-6d6d-4a40-8e9a-10bc8bdf35e3 · outbound

This paper cites B., Li, M., and Yeung, D.-Y.

Sub-Sequential Physics-Informed Learning with State Space Model B., Li, M., and Yeung, D.-Y

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4f861471-f1da-48db-983b-eab9f6f6eeac · outbound

This paper cites Failure-informed adaptive sampling for pinns.

Sub-Sequential Physics-Informed Learning with State Space Model Failure-informed adaptive sampling for pinns

Reference 10

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 7400610a-1098-4710-aefe-dcf52ff1ecf1 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Sub-Sequential Physics-Informed Learning with State Space Model Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 11

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Unavailable: canonical work link unavailable.

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Observation bd1b5fae-fb85-46e8-bc0e-e568d2c7a44d · outbound

This paper cites Hippo: Recurrent memory with optimal polynomial projections.

Sub-Sequential Physics-Informed Learning with State Space Model Hippo: Recurrent memory with optimal polynomial projections

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 2b9c7698-a448-4edf-a3ad-3d4ba4432757 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

Sub-Sequential Physics-Informed Learning with State Space Model Efficiently modeling long sequences with structured state spaces

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.727301Z digest=sha256:25609f304da2d2d7f709e59c21082c6e8c72bb20d2e7a3a18787a82993180845

Observation fe9c4c1a-5b1b-42fb-8503-eaab25387010 · outbound

This paper cites Pinnacle: A comprehensive benchmark of physics-informed neural networks for solving pdes.

Sub-Sequential Physics-Informed Learning with State Space Model Pinnacle: A comprehensive benchmark of physics-informed neural networks for solving pdes

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation a2d681f0-1019-43da-b026-29100870c615 · outbound

This paper cites Deep residual learning for image recognition.

Sub-Sequential Physics-Informed Learning with State Space Model Deep residual learning for image recognition

Reference 15

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Unavailable: canonical work link unavailable.

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Observation ce010d31-ec36-4fb4-8b7d-db08b1700c33 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Sub-Sequential Physics-Informed Learning with State Space Model Multilayer feedforward networks are universal approximators

Reference 16

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Observation da167e93-e60a-4fe3-bd96-3319e600821d · outbound

This paper cites State-space models are accurate and efficient neural operators for dynamical systems.

Sub-Sequential Physics-Informed Learning with State Space Model State-space models are accurate and efficient neural operators for dynamical systems

Reference 17

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no resolver link, observed 2026-08-09T19:31:28.745150Z

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Observation c790e1f9-c5ce-4d8d-8adb-f636ca48c223 · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 867878ec-6a04-40d1-a912-0d7bc05c9e58 · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 19

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Observation b370abe9-7569-4ef2-8ffb-f9fd1a6d3fda · outbound

This paper cites Variational Physics-Informed Neural Networks For Solving Partial Differential Equations.

Sub-Sequential Physics-Informed Learning with State Space Model Variational Physics-Informed Neural Networks For Solving Partial Differential Equations

Reference 20

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Observation cacffa6c-acb4-4e1c-95d7-4b44d8d8dc0b · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 538fa6dd-ddc5-4d5a-a67d-0220a3edab70 · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 22

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source=arxiv_source observed=2026-08-09T19:31:28.767842Z digest=sha256:9db4f941805c7392056436da330f0ae316f2e41e6d874622443ecd329c7311b8

Observation 6cec4c79-3e3e-452e-9517-b810d572dca2 · outbound

This paper cites VM amba: Visual state space model.

Sub-Sequential Physics-Informed Learning with State Space Model VM amba: Visual state space model

Reference 23

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 05e73835-61c9-44c1-9240-a492574edfbc · outbound

This paper cites KAN 2.0: Kolmogorov-Arnold Networks Meet Science.

Sub-Sequential Physics-Informed Learning with State Space Model KAN 2.0: Kolmogorov-Arnold Networks Meet Science

Reference 24

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no resolver link, observed 2026-08-09T19:31:28.775614Z

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source=arxiv_source observed=2026-08-09T19:31:28.775614Z digest=sha256:16c7d02fe68b73ca6bce2195ccf132c489f75855a2ef10fcc776ad1a88c1b698

Observation 7d62f7b3-4f10-45bb-b29f-082996b29660 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Sub-Sequential Physics-Informed Learning with State Space Model KAN: Kolmogorov-Arnold Networks

Reference 25

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Observation f0ca8800-ae76-48dc-917a-8dc41ec8d28c · outbound

This paper cites D., and Karniadakis, G.

Sub-Sequential Physics-Informed Learning with State Space Model D., and Karniadakis, G

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 67f4e272-5f78-4760-a0f9-091e22a5a31c · outbound

This paper cites and Hinton, G.

Sub-Sequential Physics-Informed Learning with State Space Model and Hinton, G

Reference 27

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no resolver link, observed 2026-08-09T19:31:28.787548Z

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Unavailable: canonical work link unavailable.

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Observation caa754e8-0096-4693-9718-a6d2383f9298 · outbound

This paper cites G., Kang, B., Katrekar, D., Li, D.

Sub-Sequential Physics-Informed Learning with State Space Model G., Kang, B., Katrekar, D., Li, D

Reference 28

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raw_fallback, observed 2026-08-09T19:31:29.324188Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation b6b59570-5c97-4bd9-bb3d-9dac757f48fc · outbound

This paper cites C., Cheng, X., Azarfar, S., Seshadri, P., Nguyen, Y.

Sub-Sequential Physics-Informed Learning with State Space Model C., Cheng, X., Azarfar, S., Seshadri, P., Nguyen, Y

Reference 29

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raw_fallback, observed 2026-08-09T19:31:29.309621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation e89eda62-216b-4f29-91be-faecac6ff493 · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 30

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raw_fallback, observed 2026-08-09T19:31:29.295027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 981a38da-dc01-4dd3-a1c6-1d7c587865b5 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Sub-Sequential Physics-Informed Learning with State Space Model Pytorch: An imperative style, high-performance deep learning library

Reference 31

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source=arxiv_source observed=2026-08-09T19:31:28.805127Z digest=sha256:85893e48c204622a8969fc2558f4443a23eafdaea2c7125dd867fcb06ae02a9a

Observation cad3397b-37a7-46fc-bcf5-4942b94de4cb · outbound

This paper cites C., Precup, D., and Lajoie, G.

Sub-Sequential Physics-Informed Learning with State Space Model C., Precup, D., and Lajoie, G

Reference 32

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raw_fallback, observed 2026-08-09T19:31:29.271279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.809711Z digest=sha256:b296d2b0671b9846fd9b0e86e2495370de506313773d7bf100eaec211eedebb6

Observation 475d16ae-6c8b-4a2a-9806-fae9bc3f26ad · outbound

This paper cites Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., and R \'e , C.

Sub-Sequential Physics-Informed Learning with State Space Model Y., Dao, T., Baccus, S., Bengio, Y., Ermon, S., and R \'e , C

Reference 33

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no resolver link, observed 2026-08-09T19:31:28.814435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T19:31:28.814435Z digest=sha256:f40ace2bffd965c86839619845fe453460e7182a767f7e9bf50ec4adfe43a909

Observation b8a49575-1e31-4c9d-905f-573f3f7d6c42 · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 34

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T19:31:28.819014Z digest=sha256:3b59fe5bc5532c86da7230da71160fb9315ae35f1fcd215ca706c0fc2f2b759d

Observation 3fe1e3d8-e816-4b9e-ad1b-b728f7815015 · outbound

This paper cites An Introduction to the Finite Element Method.

Sub-Sequential Physics-Informed Learning with State Space Model An Introduction to the Finite Element Method

Reference 35

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raw_fallback, observed 2026-08-09T19:31:29.237192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 9bedf2cb-88d8-49e6-b26e-be5fc3fc437b · outbound

This paper cites and Massey, J.

Sub-Sequential Physics-Informed Learning with State Space Model and Massey, J

Reference 36

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raw_fallback, observed 2026-08-09T19:31:29.222391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.829269Z digest=sha256:dd0c0824aebb9613e0d467fee6ee03f319b237cee383c235a83e41133e29edce

Observation 933eb405-f7a3-4584-bddd-e362cc7992b7 · outbound

This paper cites The pitfalls of simplicity bias in neural networks.

Sub-Sequential Physics-Informed Learning with State Space Model The pitfalls of simplicity bias in neural networks

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 67324d9a-c2a4-4f39-9c33-c0d820b4e1c1 · outbound

This paper cites T., Warrington, A., and Linderman, S.

Sub-Sequential Physics-Informed Learning with State Space Model T., Warrington, A., and Linderman, S

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T19:31:28.838359Z digest=sha256:83ab68c3436bde44cf9705984229cc52679f5cd82107fe79055ec0123e460475

Observation e0c6d445-384e-4a2d-934f-e9362843c998 · outbound

This paper cites Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization.

Sub-Sequential Physics-Informed Learning with State Space Model Evading the simplicity bias: Training a diverse set of models discovers solutions with superior ood generalization

Reference 39

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.842970Z digest=sha256:aed4af4e09b0c240e73e4ab1ff3743a25320df7559895a98599fe15a796ba07c

Observation 388245b0-6c39-47e9-8711-8e67f201e44c · outbound

This paper cites Is l\^ 2 physics informed loss always suitable for training physics informed neural network? Advances in Neural Information Processing Systems, 35: 0 8278--8290, 2022 a.

Sub-Sequential Physics-Informed Learning with State Space Model Is l\^ 2 physics informed loss always suitable for training physics informed neural network? Advances in Neural Information Processing Systems, 35: 0 8278--8290, 2022 a

Reference 40

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation aa84ac70-2319-4b06-89e8-d31756ed577e · outbound

This paper cites When and why pinns fail to train: A neural tangent kernel perspective.

Sub-Sequential Physics-Informed Learning with State Space Model When and why pinns fail to train: A neural tangent kernel perspective

Reference 41

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.852349Z digest=sha256:8729fc4120d9ad780456ec82b93570ccd15266e03d3e166d28cf213a7132f3d3

Observation 0f713ccb-99ba-478e-a20c-3343912d69cc · outbound

This paper cites C., Ooi, C.

Sub-Sequential Physics-Informed Learning with State Space Model C., Ooi, C

Reference 42

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.857013Z digest=sha256:1d18f09bd48c73139b36221e6f2847990e0d1efb4d5e18aa3fdf86713f760e99

Observation dd4dbdcd-cdbf-4903-8b0f-d93d7d58a05c · outbound

This paper cites A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks.

Sub-Sequential Physics-Informed Learning with State Space Model A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T19:31:29.128240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.861786Z digest=sha256:f8ce77c704bfe7471b919d02b9cc3d0107604cd9584b6f33d64582cd3c38cc99

Observation d2b81209-355b-4356-be37-88d6d8442505 · outbound

This paper cites Ro PINN : Region optimized physics-informed neural networks.

Sub-Sequential Physics-Informed Learning with State Space Model Ro PINN : Region optimized physics-informed neural networks

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T19:31:29.113623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.866584Z digest=sha256:82c7a7e32ffaa88dbbeed24170715a95cbac07384590675cd47f3fb3bbd867e2

Observation 83e84100-5d60-4981-8f53-199bdfcdc569 · outbound

This paper cites T., Yuan, Y., and Meschke, G.

Sub-Sequential Physics-Informed Learning with State Space Model T., Yuan, Y., and Meschke, G

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T19:31:29.098864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.871390Z digest=sha256:0562a16d2a6a36d5ac0954a517070c50a03086945043baa9bb6930b2f791be4d

Observation 13fd7e03-af9d-4e3e-8db5-938ca19e6e27 · outbound

This paper cites Infinite-dimensional feature interaction.

Sub-Sequential Physics-Informed Learning with State Space Model Infinite-dimensional feature interaction

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T19:31:29.084196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation eb590914-f028-4dd4-aa35-14d0c357c69b · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-09T19:31:29.068598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.880812Z digest=sha256:fdcf2d3cd72981a5a984a19b0a3e936c298d7b292c5eac9d88aac729823c1b4e

Observation 14c63301-7bc9-491e-bd87-13805dfeb016 · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 48

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.885539Z digest=sha256:cd611283faf0294f1f9162e0e94a58e7f5bd34f0b8fc849b8ed331d058f59dc0

Observation ee3c5f1b-137a-4592-a5d7-f812287f127d · outbound

This paper cites an unresolved cited work.

Sub-Sequential Physics-Informed Learning with State Space Model Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-09T19:31:29.040742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.890208Z digest=sha256:4277cf4234f1dbeb375670a2ed8601ee3c46bc4484e90298423df657500f5824

Observation 21251814-7050-4f74-917b-0993a4d3ca2d · outbound

This paper cites Alias-free mamba neural operator.

Sub-Sequential Physics-Informed Learning with State Space Model Alias-free mamba neural operator

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T19:31:29.025695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.894946Z digest=sha256:b7026a3eb6a880e1d4229199759a479393fcc9213efe7ce9a9312804bccee58e

Observation 2c442882-07ba-4ef5-bca2-5f69c701bd30 · outbound

This paper cites Vision mamba: Efficient visual representation learning with bidirectional state space model.

Sub-Sequential Physics-Informed Learning with State Space Model Vision mamba: Efficient visual representation learning with bidirectional state space model

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T19:31:29.009814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-09T19:31:28.899565Z digest=sha256:c39f69f26c72bf164661edf955c451f764f0b303490cd6c8a5a6b6e133a21246

Pith citing papers

Observation b13efeb8-4952-4e41-94b4-5944266f9907 · inbound

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks cites this paper.

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks Sub-Sequential Physics-Informed Learning with State Space Model

Reference 85

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T11:16:00.005795Z digest=sha256:d0b3bcec37a8014e46bbcf7ab83c1e63eb83a7b3d1548081dc8be8fdb5300755

Observation 0db9d52d-9e08-4855-918d-372ea51621f8 · inbound

PIANO: Physics Informed Autoregressive Network cites this paper.

PIANO: Physics Informed Autoregressive Network Sub-Sequential Physics-Informed Learning with State Space Model

Reference 34

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:29:36.929006Z digest=sha256:5e35c288a5b90c4393ec10726a2da2546b6780720aff4cbdbba19e691e374652

Observation fb632feb-d47b-4d15-a5e3-a7be04ed6e60 · inbound

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation cites this paper.

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation Sub-Sequential Physics-Informed Learning with State Space Model

Reference 2025

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 38db5de9-6ef6-4258-8ab1-220ea3b11499 · inbound

Curvature-aware dynamic precision approach for physics-informed neural networks cites this paper.

Curvature-aware dynamic precision approach for physics-informed neural networks Sub-Sequential Physics-Informed Learning with State Space Model

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-07-02T06:06:41.618886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 1e48c348-2337-4f53-9281-12e1b42ba027 · inbound

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks cites this paper.

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks Sub-Sequential Physics-Informed Learning with State Space Model

Reference 15

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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