Paper Citation Record · LEDGER

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2503.06320.

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

pith.paper-citation-record.v1
2503.06320 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:35:02.433761Z

measured 0 of 1 external citation measurements

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 55473a14-c3f2-4707-a535-83f4293bfaab · inbound

Physics-informed deep learning for infectious disease forecasting cites this paper.

Physics-informed deep learning for infectious disease forecasting Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T20:12:24.039618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2fbba761-cc2a-4c74-8066-602c8588ed5a · inbound

Equivariant Flow Matching for Symmetry-Breaking Bifurcation Problems cites this paper.

Equivariant Flow Matching for Symmetry-Breaking Bifurcation Problems Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T16:35:02.433761Z

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 2026-08-15T06:32:42.880941+00:00.