Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.18240.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:40:20.131830Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
0 of 0 outbound references displayed
External citation measurements
5
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
No outbound reference observations are available for this paper version.
Observation 7dcf7f32-5ff4-4933-baf1-8e6227af0181 · inbound
Adaptive feature capture method for solving partial differential equations with near singular solutions Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects
Reference 20
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6811c49b-20d8-4846-98dc-dd1515517ea3 · inbound
Physics-informed Fourier Basis Neural Network for Fluid Mechanics Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects
Reference 47
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f8e5a913-a718-4d66-8d02-a0099da47df4 · inbound
An adaptive wavelet-based PINN for problems with localized high-magnitude source Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects
Reference 13
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bcfe0714-f427-4926-b130-9a67e33b6805 · inbound
A numerical study into neural network surrogate model performance for uncertainty propagation Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects
Reference 10
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation b32b4643-0d7b-4ee2-8f82-82ac1e8b9b1a · inbound
Bayesian Analysis Using a Constrained Mixture of Normal-Inverse-Gamma Models Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects
Reference 172
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 71a04c13-e20d-47db-afba-feb90da11e49 · inbound
A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling Physics Informed Neural Networks (PINNs) as intelligent computing technique for solving partial differential equations: Limitation and Future prospects
Reference 6
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.