Paper Citation Record · LEDGER

Physics-informed neural networks with hard constraints for inverse design

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2102.04626.

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

pith.paper-citation-record.v1
2102.04626 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T07:32:30.510584Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:19:44.102985Z

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 f30e6d12-7e76-45f1-a83f-56f6cf19d635 · inbound

LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries cites this paper.

LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries Physics-informed neural networks with hard constraints for inverse design

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T07:32:30.510584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a9ed0d5-84bc-4fbe-8b7f-d8e598ec4df2 · inbound

Comparison of Trefftz-Based PINNs and Standard PINNs Focusing on Structure Preservation cites this paper.

Comparison of Trefftz-Based PINNs and Standard PINNs Focusing on Structure Preservation Physics-informed neural networks with hard constraints for inverse design

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:10:45.304716Z

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.

Observation 3104be15-9cf0-479d-b68e-d7bd45f525b2 · inbound

AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training cites this paper.

AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training Physics-informed neural networks with hard constraints for inverse design

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:41:23.882590Z

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.

Observation 70e3c73a-4bfc-4f8a-bb70-a6a06acb3b0a · inbound

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery cites this paper.

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery Physics-informed neural networks with hard constraints for inverse design

Reference 185

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.105049Z

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.