Paper Citation Record · LEDGER

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction

As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2505.18950.

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pith.paper-citation-record.v1
2505.18950 v1

Coverage vector

measured 38 of 38 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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

Source: arxiv_reference, observed 2026-05-14T20:39:27.840106Z

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 81dfbed9-a468-47e3-87a8-06a0a803779a · outbound

This paper cites Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction

Reference 1

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This paper cites This archetypal test model is widely used in research to study numerical simulation challenges [7, 21].

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction This archetypal test model is widely used in research to study numerical simulation challenges [7, 21]

Reference 2

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Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Unresolved cited work

Reference 3

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This paper cites Implementation We validate the PINNs and PI-DeepONets for ω = 2 πf, f = 100 Hz, a = 100, and vB = 0.2 m s−1, considering three differ- ent values of FB set as 10, 100, and 1000.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Implementation We validate the PINNs and PI-DeepONets for ω = 2 πf, f = 100 Hz, a = 100, and vB = 0.2 m s−1, considering three differ- ent values of FB set as 10, 100, and 1000

Reference 4

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Observation 7453334e-2119-4173-a62c-e5240daa5393 · outbound

This paper cites The training of PINNs follows a full-batch paradigm.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction The training of PINNs follows a full-batch paradigm

Reference 5

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Observation c0c0c8c1-7233-41ec-acab-cd67e75afa5e · outbound

This paper cites The equation-solving task is formulated as an optimization problem and carried out within the framework of physics-informed deep learning.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction The equation-solving task is formulated as an optimization problem and carried out within the framework of physics-informed deep learning

Reference 6

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Observation 372ab560-0d31-4d32-9347-265d35539404 · outbound

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

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 7

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Observation 31a1d97b-03ea-47a6-b1f2-107398de1d24 · outbound

This paper cites Learning the solution opera- tor of parametric partial differential equations with physics-informed deeponets,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Learning the solution opera- tor of parametric partial differential equations with physics-informed deeponets,

Reference 8

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Observation a5fc6b9f-0e3a-464f-9215-c6a402f48dce · outbound

This paper cites Long-time integration of parametric evolution equations with physics-informed deeponets,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Long-time integration of parametric evolution equations with physics-informed deeponets,

Reference 9

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Observation a6e5a4ea-70e9-4305-85e9-d42eaebef68e · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 10

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Observation 6fac62ea-b522-40d0-9dca-7c1cacc27c1a · outbound

This paper cites Characterizing possible failure modes in physics-informed neural networks,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Characterizing possible failure modes in physics-informed neural networks,

Reference 11

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This paper cites Respecting causality for training physics-informed neural networks,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Respecting causality for training physics-informed neural networks,

Reference 12

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This paper cites Bilbao, Numerical sound synthesis: finite difference schemes and simulation in musical acoustics.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Bilbao, Numerical sound synthesis: finite difference schemes and simulation in musical acoustics

Reference 13

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Observation 0b620c02-6cb6-4b66-a7a1-7e8521b0f2e7 · outbound

This paper cites Chaigne and J.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Chaigne and J

Reference 14

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Observation 4d2b3f7b-00e5-4e8d-8526-92f7974817be · outbound

This paper cites A physics- informed neural network approach for nearfield acoustic hologra- phy,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction A physics- informed neural network approach for nearfield acoustic hologra- phy,

Reference 15

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Observation 349d25a7-1e04-4950-9928-2a04a4406513 · outbound

This paper cites Complex-valued physics-informed neural network for near-field acoustic holography,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Complex-valued physics-informed neural network for near-field acoustic holography,

Reference 16

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Observation afcebec5-da69-467b-98ad-d887c0492662 · outbound

This paper cites Physics-Informed Neural Network-Driven Sparse Field Discretization Method for Near-Field Acoustic Holography.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Physics-Informed Neural Network-Driven Sparse Field Discretization Method for Near-Field Acoustic Holography

Reference 17

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Observation 04e4f7a8-0d97-480f-991c-97d3951ec536 · outbound

This paper cites Physics-informed neural net- work for acoustic resonance analysis in a one-dimensional acoustic tube,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Physics-informed neural net- work for acoustic resonance analysis in a one-dimensional acoustic tube,

Reference 18

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Observation b688a01f-1019-40ad-81a8-b99707960fd2 · outbound

This paper cites Physics-informed cnn for the design of acoustic equipment,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Physics-informed cnn for the design of acoustic equipment,

Reference 19

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Observation 570f7eab-55e4-49e4-a5e7-70e5df408582 · outbound

This paper cites Synthesis of voiced sounds using physics-informed neural networks,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Synthesis of voiced sounds using physics-informed neural networks,

Reference 20

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Observation b1190703-0739-4c9d-bdf4-e68e7ce47ab5 · outbound

This paper cites Acoustic Field Reconstruction in Tubes via Physics-Informed Neural Networks.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Acoustic Field Reconstruction in Tubes via Physics-Informed Neural Networks

Reference 21

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This paper cites Identification of physical prop- erties in acoustic tubes using physics-informed neural networks,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Identification of physical prop- erties in acoustic tubes using physics-informed neural networks,

Reference 22

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Observation 6498f783-60ec-4278-93ea-55a1425e645c · outbound

This paper cites Physical modelling of stiff membrane vibration using neural networks with spectral convolution layers,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Physical modelling of stiff membrane vibration using neural networks with spectral convolution layers,

Reference 23

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Observation 2fe9d471-5021-4a0b-8e13-f368e61194b3 · outbound

This paper cites Physical mod- eling using recurrent neural networks with fast convolutional layers,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Physical mod- eling using recurrent neural networks with fast convolutional layers,

Reference 24

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Observation e89ced44-dd6f-4785-a383-36565fe180d6 · outbound

This paper cites Towards efficient mod- elling of string dynamics: A comparison of state space and koopman based deep learning methods,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Towards efficient mod- elling of string dynamics: A comparison of state space and koopman based deep learning methods,

Reference 25

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Observation 813cc674-00df-4cf6-a14e-7ada0321081f · outbound

This paper cites Loss landscape engi- neering via data regulation on pinns,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Loss landscape engi- neering via data regulation on pinns,

Reference 26

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Observation d69ef24c-8141-4f8f-a736-448475b13f9f · outbound

This paper cites Efficient simulation of the bowed string in modal form,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Efficient simulation of the bowed string in modal form,

Reference 27

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Observation 956c07a5-43e7-4b27-bb02-48690b18c37a · outbound

This paper cites A comparison of friction models for bow-string interaction based on experimental measurements,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction A comparison of friction models for bow-string interaction based on experimental measurements,

Reference 28

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Observation ec9cae7d-34ff-4279-ae1c-14eff9011457 · outbound

This paper cites Understanding and mitigat- ing gradient flow pathologies in physics-informed neural networks,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Understanding and mitigat- ing gradient flow pathologies in physics-informed neural networks,

Reference 29

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This paper cites Fourier features let networks learn high frequency functions in low dimen- sional domains,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Fourier features let networks learn high frequency functions in low dimen- sional domains,

Reference 30

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Observation c53e2a8d-90e2-4cf0-9bd4-fd37144966ca · outbound

This paper cites On the spectral bias of neural net- works,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction On the spectral bias of neural net- works,

Reference 31

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Observation 84fbac94-3e40-42fb-818f-c07073ea053a · outbound

This paper cites On the eigenvector bias of fourier feature networks: From regression to solving multi-scale pdes with physics-informed neural networks,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction On the eigenvector bias of fourier feature networks: From regression to solving multi-scale pdes with physics-informed neural networks,

Reference 32

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Observation 219aacf5-9bcb-49e4-a35b-af31f2775002 · outbound

This paper cites An Expert's Guide to Training Physics-informed Neural Networks.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction An Expert's Guide to Training Physics-informed Neural Networks

Reference 33

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This paper cites SOAP: Improving and Stabilizing Shampoo using Adam.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction SOAP: Improving and Stabilizing Shampoo using Adam

Reference 34

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This paper cites Gradient align- ment in physics-informed neural networks: A second-order optimiza- tion perspective,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Gradient align- ment in physics-informed neural networks: A second-order optimiza- tion perspective,

Reference 35

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This paper cites Pyhessian: Neural networks through the lens of the hessian,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Pyhessian: Neural networks through the lens of the hessian,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:26:11.768079Z

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Observation d15b6cea-15a2-492e-bae4-bd9850e6dc78 · outbound

This paper cites Visualizing the loss landscape of neural nets,.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction Visualizing the loss landscape of neural nets,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:26:11.616310Z

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Observation 50f7e895-6d94-4f55-9d7c-45210884c6e6 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Physics-Informed Deep Learning for Nonlinear Friction Model of Bow-string Interaction On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T14:26:11.174324Z

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Pith citing papers

Observation 81dfbed9-a468-47e3-87a8-06a0a803779a · inbound