Pith. sign in

Paper Citation Record · LEDGER

Learning differentiable solvers for systems with hard constraints

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2207.08675.

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

pith.paper-citation-record.v1
2207.08675 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:36:57.926449Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T11:26:18.462950Z

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 58f1b38c-5725-499b-93b7-a8beedd22f76 · inbound

HoP: Homeomorphic Polar Learning for Hard Constrained Optimization cites this paper.

HoP: Homeomorphic Polar Learning for Hard Constrained Optimization Learning differentiable solvers for systems with hard constraints

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-09T19:36:57.926449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T19:36:57.926449Z digest=sha256:7e23585a96f174a39f2d14ac72b92ef5c741444381f039a52437b51d099dca7a

Observation 3448577d-035c-40c5-b289-f74905bb6d62 · inbound

Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media cites this paper.

Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media Learning differentiable solvers for systems with hard constraints

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-04T16:08:59.166904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T16:08:59.166904Z digest=sha256:b112b8786ca7ff2a012b636577ffdb1c317abdab07a26fc3e51566fb8ace54e1

Observation c9134ddd-88f2-40f8-8d6a-8e2da460b3e5 · inbound

Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling cites this paper.

Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling Learning differentiable solvers for systems with hard constraints

Reference 79

Resolution
verified exact
arxiv_id, observed 2026-05-18T11:26:18.465244Z

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=2026-05-18T11:26:06.622876Z digest=sha256:516f1426078ecc16b8f57d0fc374d342ec440cd126f8b098666532c3dd81abcb

Observation a1e2bea5-0e05-41e9-b796-cfe50a6f8c43 · inbound

Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks cites this paper.

Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks Learning differentiable solvers for systems with hard constraints

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-03T22:46:59.135854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:46:59.135854Z digest=sha256:3a76c34c499b421a136e628b072e408abb3e35642052992bb38bba7682e39717

Observation e3d37d29-1456-4f5a-ba6d-a75d9ff57e2d · inbound

End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers cites this paper.

End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers Learning differentiable solvers for systems with hard constraints

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-01T09:44:50.689299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T09:44:50.689299Z digest=sha256:bd0f181020e7c026fb22be0cf832b2db9fe95e81a8d84350b0146cd2db390b0e