Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2503.17379.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:29:43.683788Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T11:58:22.761695Z
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 01882c1b-684a-4052-a8df-c461c36786c7 · inbound
Convolution-weighting method for the physics-informed neural network: A Primal-Dual Optimization Perspective cites this paper.
Convolution-weighting method for the physics-informed neural network: A Primal-Dual Optimization Perspective Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:29:43.683788Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ec4ed5c3-38e4-4211-8bf9-1af01d0ce467 · inbound
Discontinuity-aware KAN-based physics-informed neural networks cites this paper.
Discontinuity-aware KAN-based physics-informed neural networks Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:26:50.321992Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 02ba2342-7c7b-464f-8b56-d031f1118f73 · inbound
CLINN: Conservation Law Informed Neural Network for Approximating Discontinuous Solutions cites this paper.
CLINN: Conservation Law Informed Neural Network for Approximating Discontinuous Solutions Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T11:58:22.766606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3f6ff3f1-df35-4219-a72b-a603de37f7c8 · 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 Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T16:08:57.258852Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.