Paper Citation Record · LEDGER

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models

As of 17 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2501.03911.

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

pith.paper-citation-record.v1
2501.03911 v1

Coverage vector

measured 33 of 33 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 16995954-f3dd-456b-8355-155bb90e665f · outbound

This paper cites A fast adaptive PD-FEM coupling model for predicting cohesive crack growth.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models A fast adaptive PD-FEM coupling model for predicting cohesive crack growth

Reference 1

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Observation 6738a9a8-7aa1-4b48-b66c-598f0c64fa44 · outbound

This paper cites Bandai and T.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Bandai and T

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:20.074751Z

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Observation 61bf7f83-4f00-4873-8749-66c26cc1d216 · outbound

This paper cites A Neural Probabilistic Language Model.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models A Neural Probabilistic Language Model

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:19.954763Z

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Observation c6a57d74-c985-459f-afdb-43995653e4be · outbound

This paper cites Berardi, F.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Berardi, F

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:19.834753Z

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Observation 4d2f0bf3-b2f9-46cc-a813-081a55b36851 · outbound

This paper cites Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:19.674750Z

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Observation 5d8959bd-c2a2-49cf-82dd-350d725a8a1f · outbound

This paper cites Physics-informed neural net- works for inverse problems in nano-optics and metamaterials.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Physics-informed neural net- works for inverse problems in nano-optics and metamaterials

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:19.558330Z

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Observation 469d6ace-410f-4cb3-ad2b-0d1943435e30 · outbound

This paper cites Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Reference 7

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Observation 198b86a6-780b-4bdb-896f-7d38c7f42ecf · outbound

This paper cites Difonzo, Luciano Lopez, and Sabrina F.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Difonzo, Luciano Lopez, and Sabrina F

Reference 8

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Observation 07e33b6f-ca8c-46e8-b5c5-9226e99601bc · outbound

This paper cites an unresolved cited work.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Unresolved cited work

Reference 9

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Observation ce733d19-0d0c-4ce4-b500-255fc858588a · outbound

This paper cites Emmrich and D.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Emmrich and D

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:19.174751Z

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Observation 1c8dd03b-9749-4301-a039-928eae3c6d94 · outbound

This paper cites Grohs and G.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Grohs and G

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:19.004742Z

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Observation a71e56f0-dcc0-494c-a315-e90f9376c154 · outbound

This paper cites A nonlocal physics- informed deep learning framework using the peridynamic differential operator.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models A nonlocal physics- informed deep learning framework using the peridynamic differential operator

Reference 12

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Observation 68f63c68-56a6-4f32-b2a6-028dd4a644bc · outbound

This paper cites Jafarzadeh, A.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Jafarzadeh, A

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:18.764753Z

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Observation 80884249-4705-4ab2-b953-abc5e8b0ac6a · outbound

This paper cites Peridynamic neural operators: A data-driven nonlocal constitutive model for complex material responses.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Peridynamic neural operators: A data-driven nonlocal constitutive model for complex material responses

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:18.644625Z

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Observation eeca5a8c-5470-473e-a2f6-b0c13f6b10b2 · outbound

This paper cites Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements

Reference 15

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Source-reported events for the cited work

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Observation 432341f3-b057-48d9-9d03-83c85dee26f5 · outbound

This paper cites Loss landscapes and optimization in over- parameterized non-linear systems and neural networks.Applied and Computational Harmonic Anal- ysis, 59:85–116, 2022.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Loss landscapes and optimization in over- parameterized non-linear systems and neural networks.Applied and Computational Harmonic Anal- ysis, 59:85–116, 2022

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:18.504758Z

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Observation 926e559e-5dcd-4615-8926-768475a11b46 · outbound

This paper cites Lopez and S.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Lopez and S

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:18.374741Z

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Observation 2022dd5a-0c09-4156-a451-c00e4fd26185 · outbound

This paper cites Lopez and S.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Lopez and S

Reference 18

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Observation 3064d114-ffbe-49f9-a44b-fd05d9cc80f5 · outbound

This paper cites Computation of Eigenvalues for Nonlocal Models by Spectral Methods.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Computation of Eigenvalues for Nonlocal Models by Spectral Methods

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:18.089102Z

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Observation 025426ee-ded7-4c36-8cd1-0ffbced40f02 · outbound

This paper cites Madenci and E.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Madenci and E

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:17.934548Z

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Observation b1dd15d1-5637-4a70-82d2-07de50f6140a · outbound

This paper cites Mavi, A.C.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Mavi, A.C

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:17.814746Z

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Observation 81e96563-2483-4b85-9d3f-9646cf121430 · outbound

This paper cites Raissi, P.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Raissi, P

Reference 22

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Observation 8d40e8f3-5c4d-4daf-a0d6-f1de90c0de3f · outbound

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Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Unresolved cited work

Reference 23

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Observation d4e4c8b5-35e5-4f8d-95bc-3efb432141a3 · outbound

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Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Unresolved cited work

Reference 24

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Observation 33e53d00-776f-4402-9fb8-092461667b34 · outbound

This paper cites Sukumar and Ankit Srivastava.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Sukumar and Ankit Srivastava

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:17.214749Z

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Observation b4812d0f-be86-40db-910c-fe287d83f17e · outbound

This paper cites Vitullo, A.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Vitullo, A

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:17.034746Z

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Observation 12c39937-7289-40d3-bcec-081e4387d665 · outbound

This paper cites an unresolved cited work.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Unresolved cited work

Reference 27

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Observation 4e2f7a0f-86ac-4ec8-b86f-e7baabf9328e · outbound

This paper cites Weckner and R.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Weckner and R

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:16.776329Z

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Observation 6030fc9b-dd79-4d1d-996c-17fe744507d9 · outbound

This paper cites Transfer learning based physics- informed neural networks for solving inverse problems in engineering structures under different loading scenarios.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Transfer learning based physics- informed neural networks for solving inverse problems in engineering structures under different loading scenarios

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:16.666808Z

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Observation 5eef9b2d-0f90-4d41-99ef-02378e32cc7b · outbound

This paper cites Two-dimensional double horizon peridynamics for membranes.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Two-dimensional double horizon peridynamics for membranes

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:16.604748Z

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Observation 0d96aa39-feb8-4722-84b3-1932413c09b4 · outbound

This paper cites an unresolved cited work.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:51:16.515201Z

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

Observation 5d2b0eb2-7bc7-41cd-8fc9-ef81f18e3da4 · outbound

This paper cites Zaccariotto, T.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Zaccariotto, T

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:16.364752Z

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Observation 815fb3e6-59e8-4156-b858-68456a2d3b83 · outbound

This paper cites Mauro Picone.

Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models Mauro Picone

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:51:16.210974Z

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

Pith citing papers

No inbound Pith citation observations are available.