Paper Citation Record · LEDGER

Understanding and mitigating gradient pathologies in physics-informed neural networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2001.04536.

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

pith.paper-citation-record.v1
2001.04536 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:09:36.242520Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T08:06:03.917444Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation eb749171-9b79-47da-a60e-94b5f7f12847 · inbound

Universal Differential Equations for Scientific Machine Learning cites this paper.

Universal Differential Equations for Scientific Machine Learning Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T00:24:43.322409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8f109f92-9b2f-45f6-9b51-6a74cdf1a01c · inbound

Bayesian Reasoning for Physics Informed Neural Networks cites this paper.

Bayesian Reasoning for Physics Informed Neural Networks Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:06:03.919345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1a5a26c5-800f-4d08-8a84-455edc8b36e0 · inbound

Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks cites this paper.

Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 5237

Resolution: unresolved; no resolver link, observed 2026-08-08T13:09:36.242520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4270b525-3a80-48b4-b084-2e11e9377bf9 · inbound

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs cites this paper.

Over-PINNs: Enhancing Physics-Informed Neural Networks via Higher-Order Partial Derivative Overdetermination of PDEs Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:48.347061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e677b092-1fc0-473d-8a94-dc261b05bbc2 · inbound

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature cites this paper.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:29.508093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49878b61-784b-42fd-906c-795ba75fce85 · inbound

FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning cites this paper.

FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T16:06:35.236140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 22923ef4-4cbc-4bd3-a783-9b39a2fc9d7d · inbound

Active learning with physics-informed neural networks for optimal sensor placement in deep tunneling through transversely isotropic elastic rocks cites this paper.

Active learning with physics-informed neural networks for optimal sensor placement in deep tunneling through transversely isotropic elastic rocks Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T20:19:20.713513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a4ec2dc-df90-452d-9c5e-dcb85f8f626b · inbound

Quantum-Enhanced Convergence of Physics-Informed Neural Networks cites this paper.

Quantum-Enhanced Convergence of Physics-Informed Neural Networks Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T12:10:53.509269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 71a96b9b-f0a3-4a38-852c-55292df80b03 · inbound

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos cites this paper.

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:16:21.179494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 796ecd59-d3d7-4c55-a744-a052698162e4 · inbound

Physics-Informed Neural Networks for Solving Two-Flavor Neutrino Oscillations in Vacuum and Matter Environments for Atmospheric and Reactor Neutrinos cites this paper.

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:18:04.089468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6c9ac531-c2eb-4ee4-b0dc-3666b8a2b7cd · inbound

Physics informed operator learning of parameter dependent spectra cites this paper.

Physics informed operator learning of parameter dependent spectra Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:26:14.440354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4a07786e-04fd-44b0-89b2-b32a6da129e7 · inbound

StableGrad: Backward Scale Control without Batch Normalization cites this paper.

StableGrad: Backward Scale Control without Batch Normalization Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:53:06.021234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8586f6de-cdd7-4f27-a344-c500176dc3e2 · inbound

Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction cites this paper.

Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:21:13.020445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6260bfbc-383f-4481-babb-6b7d113718be · inbound

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks cites this paper.

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T02:27:16.817511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.