Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2503.06320.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:55:46.467626Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T11:07:47.074820Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
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 16c3f96a-8df3-4c14-aa30-6fc0a7c59af8 · inbound
Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks cites this paper.
Repulsive Ensembles for Bayesian Inference in Physics-informed Neural Networks Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:55:46.467626Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7ba50993-ede4-4194-803b-d4a7a9512ee7 · inbound
Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs cites this paper.
Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:05:33.351784Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 750bb373-49e9-42ea-a86d-5d31fad01938 · inbound
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics cites this paper.
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T12:18:55.138576Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4880b227-afff-4d69-bb1d-0dedace25ba2 · inbound
Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation cites this paper.
Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T11:07:47.077073Z
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.