Paper Citation Record · LEDGER

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers

As of 20 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2606.25323.

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

pith.paper-citation-record.v1
2606.25323 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-25T20:21:59.870759Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

19 of 19 outbound references displayed

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External citation measurements

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Outbound references

Observation 6351cd9a-fcae-47ad-9740-69c1c3407d06 · 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 Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 662710b7-eef5-477b-9dcc-71f39d32e300 · outbound

This paper cites Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 90a4a403-cde5-4ac9-9c9c-a9d794159eb1 · outbound

This paper cites Jin,The Finite Element Method in Electromagnetics, 3rd ed., Hoboken, NJ, USA: Wiley, 2014.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Jin,The Finite Element Method in Electromagnetics, 3rd ed., Hoboken, NJ, USA: Wiley, 2014

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 8f6bfdce-e17f-4ea5-8ae5-315a673db572 · outbound

This paper cites A perfectly matched layer for the absorption of electromagnetic waves.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers A perfectly matched layer for the absorption of electromagnetic waves

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation b3cef070-6b0a-4ac8-b5d9-040594a225b7 · outbound

This paper cites An anisotropic perfectly matched layer—absorbing medium for the truncation of FDTD lattices.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers An anisotropic perfectly matched layer—absorbing medium for the truncation of FDTD lattices

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 71cdaf71-ea4d-4b9a-9473-2768400a6dcb · outbound

This paper cites A Maxwell’s equations–based deep learning method for time-domain electromagnetic simulations.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers A Maxwell’s equations–based deep learning method for time-domain electromagnetic simulations

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation ca369b14-df02-4b10-8959-741731f133f1 · outbound

This paper cites A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation bb54c943-399b-4138-92b2-c97609999d1a · outbound

This paper cites Random Weight Factorization Improves the Training of Continuous Neural Representations.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Random Weight Factorization Improves the Training of Continuous Neural Representations

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:20:07.143797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 58f23912-d423-479d-9354-f863e05bddd2 · outbound

This paper cites On the limited memory BFGS method for large scale optimization.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers On the limited memory BFGS method for large scale optimization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 0d6c111a-aa4a-4e6f-ac6f-e2851c06d58e · outbound

This paper cites Challenges in training PINNs: A loss landscape perspective.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Challenges in training PINNs: A loss landscape perspective

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 9c29eede-e010-4552-abc3-ac7d61fb0084 · outbound

This paper cites Self-scaling variable metric (SSVM) algorithms. Part I: Criteria and sufficient conditions for scaling a class of algorithms.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Self-scaling variable metric (SSVM) algorithms. Part I: Criteria and sufficient conditions for scaling a class of algorithms

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 8823bd72-7729-4d6e-8cf4-5c442ae8ff99 · outbound

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

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Respecting causality for training physics-informed neural networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 3a1b9193-8762-4480-9860-c988dda8824f · outbound

This paper cites Causality-enhanced discrete physics-informed neural networks for predicting evolutionary equations.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Causality-enhanced discrete physics-informed neural networks for predicting evolutionary equations

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 4b0c1929-e2fd-4685-819f-f2b2973f9c84 · outbound

This paper cites Implementation of Maxwell’s equations solving algorithm based on PINN.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Implementation of Maxwell’s equations solving algorithm based on PINN

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 509906c5-aa50-4ce8-8233-90128f03d9a6 · outbound

This paper cites Physics-Informed Neural Networks for the Numerical Modeling of Steady-State and Transient Electromagnetic Problems with Discontinuous Media.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Physics-Informed Neural Networks for the Numerical Modeling of Steady-State and Transient Electromagnetic Problems with Discontinuous Media

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:20:07.135529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c19c44a8-f663-4001-a202-92490ce0e95e · outbound

This paper cites PINNs for solving unsteady Maxwell’s equations: Convergence issues and comparative assessment with compact schemes.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers PINNs for solving unsteady Maxwell’s equations: Convergence issues and comparative assessment with compact schemes

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Observation 90c6d5b0-d8ab-4d2b-9644-513c0738e9ea · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Adam: A Method for Stochastic Optimization

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-04T20:20:07.139592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1b4acc8-d59a-4ef6-82f8-8ddc08bf8c47 · outbound

This paper cites Deep Reinforcement Learning via L-BFGS Optimization.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers Deep Reinforcement Learning via L-BFGS Optimization

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-04T20:20:07.145874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 95fae7cb-a9c7-4c79-99a8-e1905f7b6140 · outbound

This paper cites DeepXDE: A deep learning library for solving differential equations.

Physics-Informed Neural Networks for the Time-Domain Maxwell Equations with Split-Field Perfectly Matched Layers DeepXDE: A deep learning library for solving differential equations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-25T20:21:59.870759Z

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Pith citing papers

No inbound Pith citation observations are available.