Paper Citation Record · LEDGER

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification

As of 23 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2505.04263.

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

pith.paper-citation-record.v1
2505.04263 v1

Coverage vector

measured 22 of 22 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-15T23:40:58.093931Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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External citation measurements

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Outbound references

Observation f3571d34-cd13-41af-ab77-c244d8332d22 · outbound

This paper cites Finally, using once more the definition ofwk, cf.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Finally, using once more the definition ofwk, cf

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:58.321618Z

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Observation b7259fce-9d1b-4663-940a-24839a96a9d9 · outbound

This paper cites WWU::123155.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification WWU::123155

Reference 3

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Observation 830513c4-0b3c-4a30-b15e-fafa42c6fc72 · outbound

This paper cites A deep learning framework for solution and discovery in solid mechanics.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification A deep learning framework for solution and discovery in solid mechanics

Reference 7

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Observation d99710f8-1778-40aa-855b-fac26c63f09f · outbound

This paper cites Solid lines report training loss of various terms, dashed lines report validation loss.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Solid lines report training loss of various terms, dashed lines report validation loss

Reference 8

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Observation 3a378d59-b76e-446b-8283-6f5abe5146e2 · outbound

This paper cites URL http://dx.doi.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification URL http://dx.doi

Reference 10

Resolution: verified exact; doi, observed 2026-08-15T23:40:58.192307Z

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Observation b398eb4d-1ff5-4dab-993d-2443ea523a8e · outbound

This paper cites doi: 10.1038/s41598-019-51539-5.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification doi: 10.1038/s41598-019-51539-5

Reference 11

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Observation 422dbea1-1f8d-474e-9f10-ff18f583a360 · outbound

This paper cites an unresolved cited work.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation dccc4da2-d3e3-4482-b2d8-80348c2717f7 · outbound

This paper cites S., Venzke, A., and Chatzivasileiadis, S.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification S., Venzke, A., and Chatzivasileiadis, S

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:58.343795Z

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Observation 52ea0d07-ac11-4379-a092-bcaff426bb33 · outbound

This paper cites Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data

Reference 14

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Observation f0ec402c-5cbf-4d35-a119-1854568bfb13 · outbound

This paper cites Physics informed deep learning for computational elastodynamics without labeled data.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Physics informed deep learning for computational elastodynamics without labeled data

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:40:58.219641Z

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Observation a3bb0739-d978-44ad-be87-5f90aa4d7a12 · outbound

This paper cites Proof of Theorem 2.2 The proof is based on extending ideas from (Gomes et al., 2019; Burger et al., 2020), where in- and outflow boundary conditions are treated, to metric graphs.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Proof of Theorem 2.2 The proof is based on extending ideas from (Gomes et al., 2019; Burger et al., 2020), where in- and outflow boundary conditions are treated, to metric graphs

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:58.333199Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2da77ad2-39ba-4fe6-bda1-8b1b2f9f4fbb · outbound

This paper cites These are popular discretization schemes as they usually work in a structure preserving manner.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification These are popular discretization schemes as they usually work in a structure preserving manner

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:58.310415Z

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Observation 7dc77a6d-6265-4e7a-a093-9b7ee4480f91 · outbound

This paper cites To solve the system of ordinary differential equations (26) for the unknowns ρe k andρv, respectively, we introduce the following time-discretization.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification To solve the system of ordinary differential equations (26) for the unknowns ρe k andρv, respectively, we introduce the following time-discretization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:58.298612Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d5049415-ad98-48af-becc-f31e62a23482 · outbound

This paper cites With similar arguments like before we conclude that the right-hand side is non- negative and thus, 1−⃗ ρn≥ 0, which proves the upper bound.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification With similar arguments like before we conclude that the right-hand side is non- negative and thus, 1−⃗ ρn≥ 0, which proves the upper bound

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:58.287624Z

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Observation b9d713b7-b8a2-44fd-831c-fb279caf2770 · outbound

This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 2002

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Observation 9acea787-f943-4970-b57a-6e90c6e45390 · outbound

This paper cites Universal Differential Equations for Scientific Machine Learning.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Universal Differential Equations for Scientific Machine Learning

Reference 2006

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Observation aee34594-2401-4011-85e7-c02d2c530e6c · outbound

This paper cites and Karniadakis, G.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification and Karniadakis, G

Reference 2011

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:58.353728Z

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Observation 752f9c83-2b58-439f-8094-bc9f5fd36c97 · outbound

This paper cites URL https://dx.doi.org/10.1088/ 1751-8113/49/34/345602.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification URL https://dx.doi.org/10.1088/ 1751-8113/49/34/345602

Reference 2016

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Observation cc5671d6-fb0f-4fe8-bb0f-9731745609a3 · outbound

This paper cites doi: https://doi.org/10.1016/j.arcontrol.2017.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification doi: https://doi.org/10.1016/j.arcontrol.2017

Reference 2017

Resolution: verified exact; doi, observed 2026-08-15T23:40:58.126314Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9c0df537-42f9-4580-8c34-3e1dbaf890a3 · outbound

This paper cites Gyrya, V.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Gyrya, V

Reference 2019

Resolution: verified exact; doi, observed 2026-08-15T23:40:58.151948Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1d31bfd5-2585-44b0-815a-dd22a046f9ba · outbound

This paper cites an unresolved cited work.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification Unresolved cited work

Reference 2020

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Observation 74cdc8a3-c80a-4155-acef-3a825deaad20 · outbound

This paper cites doi: https://doi.org/10.1016/j.jde.2025.02.

Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification doi: https://doi.org/10.1016/j.jde.2025.02

Reference 2025

Resolution: verified exact; doi, observed 2026-08-15T23:40:58.163390Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.