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Paper Citation Record · LEDGER

Deep Ritz revisited

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1912.03937.

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

pith.paper-citation-record.v1
1912.03937 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:19:23.922625Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T11:04:37.842233Z

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 ab565339-c456-4af7-90a8-35173f60de80 · inbound

PINN-DG: Residual neural network methods trained with Finite Elements cites this paper.

PINN-DG: Residual neural network methods trained with Finite Elements Deep Ritz revisited

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T20:19:23.922625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:19:23.922625Z digest=sha256:8771ca3051320e0612a49946727cbe6f33850a92acfc64596edf4898b220fccc

Observation 8cfa78d1-386e-42c2-bb52-82336f4757bf · inbound

Deep learning-based phase-field modelling of brittle fracture in anisotropic media cites this paper.

Deep learning-based phase-field modelling of brittle fracture in anisotropic media Deep Ritz revisited

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-15T07:15:11.952542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T07:11:38.960960Z digest=sha256:7236e691b026a28da1029cfe1f63027a8cba68f6765c83f756a7282cf50ddae4

Observation 935b9343-1a18-4368-b48e-312e0a6654d1 · inbound

The Coercivity Gap in Neural PDE Solvers: Parameter Escape and Functional Convergence cites this paper.

The Coercivity Gap in Neural PDE Solvers: Parameter Escape and Functional Convergence Deep Ritz revisited

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-06-30T11:04:37.843546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T10:55:56.415184Z digest=sha256:a4f739a5cb88768f8312725ae06659ccb9d4c4835f4ff17fbad7825dd16910a6