Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T22:46:59.143229Z
Paper Citation Record · LEDGER
Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks
As of 8 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 0 inbound Pith citation observations for arXiv:2511.09048.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
5 of 5 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 6de7b2cb-75d2-4823-854f-55ae1ac49788 · outbound
This paper cites Challenges in Training PINNs: A Loss Landscape Perspective.
Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks Challenges in Training PINNs: A Loss Landscape Perspective
Reference 2019
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T22:46:59.139134Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1e2bea5-0e05-41e9-b796-cfe50a6f8c43 · outbound
This paper cites Learning differentiable solvers for systems with hard constraints.
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.
Observation eedc08e5-2ee9-4477-9528-4351ae21b3fb · outbound
This paper cites Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws.
Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T22:46:59.132207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30c26e07-68a8-40bf-819d-66d383136c38 · outbound
This paper cites Lagrangian neural networks.
Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks Lagrangian neural networks
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T22:46:59.128106Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0b46ec34-933d-4f73-8b0b-644d82f0b632 · outbound
This paper cites Each 2D PDE dataset had parameters ∆x= 1/16,X= [0,2],∆t= 0.01,T= [0,0.99],(n x, ny)= (32, 32), andn t = 100, so each had 25,600 total state valuesu.
Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks Each 2D PDE dataset had parameters ∆x= 1/16,X= [0,2],∆t= 0.01,T= [0,0.99],(n x, ny)= (32, 32), andn t = 100, so each had 25,600 total state valuesu
Reference 4090
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T22:46:59.143229Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.