Paper Citation Record · LEDGER

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2411.11801.

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

pith.paper-citation-record.v1
2411.11801 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:12:18.907241Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:31:38.773046Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T23:45:31.018733Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy28
unresolved14
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca1b39bc-ac08-4b3f-9e5e-5cdc1528615d · outbound

This paper cites Nonlinear differential equations and dynamical systems.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Nonlinear differential equations and dynamical systems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.904619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bd2a467a-3371-4683-9737-9e2ce4fc7e61 · outbound

This paper cites Differential equations and dynamical systems , volume 7.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Differential equations and dynamical systems , volume 7

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.591608Z

Source-reported events for the cited work

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Observation 43bd47c9-b818-451a-975a-5e28397ba085 · outbound

This paper cites Differential equations, dynamical systems, and an introduction to chaos.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Differential equations, dynamical systems, and an introduction to chaos

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.875925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f548b403-abc0-4a08-bd3d-ad8fb5084ebc · outbound

This paper cites Semi-supervised structural linear/nonlinear damage detection and characteriza- tion using sparse identification.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Semi-supervised structural linear/nonlinear damage detection and characteriza- tion using sparse identification

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.857836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74840f5d-5320-425b-a1ae-133bf4303e00 · outbound

This paper cites an unresolved cited work.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:12:19.838796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 28b06f60-5888-4421-993e-e1659a53a74e · outbound

This paper cites Data-driven discovery of partial differential equations.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Data-driven discovery of partial differential equations

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.617321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d325e29-303f-4eb0-ab02-8098ef05a327 · outbound

This paper cites Data-driven science and engineering: Machine learning, dynamical systems, and control.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Data-driven science and engineering: Machine learning, dynamical systems, and control

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.631796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f6b8b9d-ce8e-4a96-a75d-2ea974351d39 · outbound

This paper cites A data–driven approximation of the koopman operator: Extending dynamic mode decomposition.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems A data–driven approximation of the koopman operator: Extending dynamic mode decomposition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.795267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9dfb271f-6711-4e5d-ac12-dcc1dd2f28e9 · outbound

This paper cites A time domain approach for identifying nonlinear vibrating structures by subspace methods.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems A time domain approach for identifying nonlinear vibrating structures by subspace methods

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.775770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d110c3d0-0f53-4c3d-8adc-46b0ef671205 · outbound

This paper cites Dynamic mode decomposition with control.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Dynamic mode decomposition with control

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.652698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a7cac53-5ddc-49fe-b0d8-813aa8e846a9 · outbound

This paper cites Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.744833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 61841b70-77bb-479b-b076-50e142de2c34 · outbound

This paper cites System identification of pem fuel cells using an improved elman neural network and a new hybrid optimization algorithm.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems System identification of pem fuel cells using an improved elman neural network and a new hybrid optimization algorithm

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.724526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3536503-bd8c-49a8-86d0-524db3f823cd · outbound

This paper cites Deep learning and artificial neural networks for spacecraft dynamics, navigation and control.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Deep learning and artificial neural networks for spacecraft dynamics, navigation and control

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.706593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ba82721-5e69-4c2b-b7c5-a921fbf074be · outbound

This paper cites Deep convolutional neural network for structural dynamic response estimation and system identification.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Deep convolutional neural network for structural dynamic response estimation and system identification

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.689585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cd014fe5-2ba9-4dce-a271-7d2d13f0b403 · outbound

This paper cites Randomized algorithms for nonlinear system identification with deep learning modification.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Randomized algorithms for nonlinear system identification with deep learning modification

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.669616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ac60b55-f5f8-4cdc-a382-d4543a2da3b6 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Fourier Neural Operator for Parametric Partial Differential Equations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.703658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64998d33-df98-43fd-aa6a-19571a98b1e8 · outbound

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

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.644836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b2001dde-9b7e-4ac2-a5e1-29dece17079a · outbound

This paper cites Learning partial differential equations via data discovery and sparse optimization.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Learning partial differential equations via data discovery and sparse optimization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.716348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fc916c9-276b-4fb6-9502-f5d8bce726dd · outbound

This paper cites On spike-and-slab priors for bayesian equation discovery of nonlinear dynamical systems via sparse linear regression.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems On spike-and-slab priors for bayesian equation discovery of nonlinear dynamical systems via sparse linear regression

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.608488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0517b480-9935-4f9b-8fd1-b76358f8242a · outbound

This paper cites Physics-informed ai and ml-based sparse system identifi- cation algorithm for discovery of pde’s representing nonlinear dynamic systems.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Physics-informed ai and ml-based sparse system identifi- cation algorithm for discovery of pde’s representing nonlinear dynamic systems

Reference 20

Resolution: verified exact; raw_fallback, observed 2026-08-12T18:12:19.120619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 714d7a04-9f8d-4fb2-b1af-77eadbb43f7f · outbound

This paper cites Symbolic genetic algorithm for discovering open-form partial differential equations (sga-pde).

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Symbolic genetic algorithm for discovering open-form partial differential equations (sga-pde)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.574736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2f5cfffd-af1e-427e-b013-4e217cc18887 · outbound

This paper cites an unresolved cited work.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-12T18:12:19.550168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 97a6e270-714f-4c96-a54a-53199ff10652 · outbound

This paper cites Structural damage identification by extended k alman filter with l 1-norm regularization scheme.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Structural damage identification by extended k alman filter with l 1-norm regularization scheme

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.529301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 79f4d6c7-d3f3-4ca6-9ad4-43ee725c565a · outbound

This paper cites Pde-net: Learning pdes from data.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Pde-net: Learning pdes from data

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.756088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbc88677-e8ad-4c88-a23f-2a31fb556738 · outbound

This paper cites Pde-net 2.0: Learning pdes from data with a numeric-symbolic hybrid deep network.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Pde-net 2.0: Learning pdes from data with a numeric-symbolic hybrid deep network

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.488687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 764b2301-3b68-40fa-aea6-e7f146c37796 · outbound

This paper cites Deepmod: Deep learning for model discovery in noisy data.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Deepmod: Deep learning for model discovery in noisy data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.469090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b3e3129b-968a-423b-93fb-a0ea8d5a95c8 · outbound

This paper cites Robust discovery of partial differential equations in complex situations.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Robust discovery of partial differential equations in complex situations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.448628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 15188894-bba1-43d7-bb11-60a74a71e917 · outbound

This paper cites A hybrid modelling approach to model process dynamics by the discovery of a system of partial differential equations.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems A hybrid modelling approach to model process dynamics by the discovery of a system of partial differential equations

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.428869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation da845e55-b9ff-4c0c-946f-52442048e31a · outbound

This paper cites Pde-read: Human-readable partial differential equation discovery using deep learning.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Pde-read: Human-readable partial differential equation discovery using deep learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.410729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e1fe150b-e9c9-43a9-a205-b23065aa8694 · outbound

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

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Hidden physics models: Machine learning of nonlinear partial differential equations

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.390767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c5131923-0c82-48bf-afea-be4029633cad · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.809982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eb2eecd-8e03-4cd1-a0bd-2c48937619d6 · outbound

This paper cites A review of physics-informed machine learning in fluid mechanics.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems A review of physics-informed machine learning in fluid mechanics

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.820385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e2c1975-060c-4a45-8f3c-57280762cdd1 · outbound

This paper cites Parsimony-enhanced sparse bayesian learning for robust discovery of partial differential equations.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Parsimony-enhanced sparse bayesian learning for robust discovery of partial differential equations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.335776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 02fa7d4a-aa7e-49de-a6ad-311642906431 · outbound

This paper cites Data-and theory-guided learning of partial differ- ential equations using simultaneous basis function approximation and parameter estimation (snape).

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Data-and theory-guided learning of partial differ- ential equations using simultaneous basis function approximation and parameter estimation (snape)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.313340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb8cc0da-2601-423f-b595-539dc0fcbf58 · outbound

This paper cites Development of inter- pretable, data-driven plasticity models with symbolic regression.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Development of inter- pretable, data-driven plasticity models with symbolic regression

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.295581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5e8bc0f7-bbfc-48d8-b507-22f771553e77 · outbound

This paper cites Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.268415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 674d6fd4-1746-4ada-9097-7be15a120579 · outbound

This paper cites Data-driven discovery of formulas by symbolic regression.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Data-driven discovery of formulas by symbolic regression

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.246015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 21c791ea-adc8-45f6-92f9-83d8728cdaf1 · outbound

This paper cites The data-driven discovery of partial differential equations by symbolic genetic algorithm.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems The data-driven discovery of partial differential equations by symbolic genetic algorithm

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.225198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aec2e072-37e1-4c83-9f10-64a6c0e3b5ef · outbound

This paper cites Sparse identification of nonlinear dynamical systems via reweighted l1-regularized least squares.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Sparse identification of nonlinear dynamical systems via reweighted l1-regularized least squares

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.200864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 17c90b7b-3075-4703-b407-4475fa972f4d · outbound

This paper cites Nonlinear dynamical system identification using the sparse regression and separable least squares methods.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Nonlinear dynamical system identification using the sparse regression and separable least squares methods

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:12:19.175058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 24aecd15-7cf7-44fb-8fc3-1df8a1a92e31 · outbound

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

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems Data-driven discovery of coordinates and governing equations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.890424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4867b885-04b7-4f36-b613-1b593d39c1c4 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems KAN: Kolmogorov-Arnold Networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.896972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08226344-1a9d-4ab5-8b87-0e112234230d · outbound

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

KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems KAN 2.0: Kolmogorov-Arnold Networks Meet Science

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T18:12:18.907241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4f2900a3-188e-42b0-8c06-1327eac47ec3 · inbound

SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies cites this paper.

SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-10T23:45:31.024527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74ef14ad-7f00-49e4-aaae-4d8238493577 · inbound

State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification cites this paper.

State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systems

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:38.773046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.