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Paper Citation Record · LEDGER

About optimal loss function for training physics-informed neural networks under respecting causality

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2304.02282.

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

pith.paper-citation-record.v1
2304.02282 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:55:34.233272Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T23:15:25.128244Z

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 7f2d591b-cac5-48b4-a4ee-d5704b89c266 · inbound

Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks? cites this paper.

Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks? About optimal loss function for training physics-informed neural networks under respecting causality

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T00:55:34.233272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:55:34.233272Z digest=sha256:74433ea3e0915a3a3b0a1785c3c002d591d61183cab7fd4588cd289e827f16d8

Observation fbb9fc17-fa66-49b0-bbb6-529a7f7000ba · inbound

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks cites this paper.

About rectified sigmoid function for enhancing the accuracy of Physics-Informed Neural Networks About optimal loss function for training physics-informed neural networks under respecting causality

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-10T23:15:25.133004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:15:24.593343Z digest=sha256:57c66db52bfdbd4fa1681222d064b3984e4b53b7fc47fa464bc46bcd862c8ad1