Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2202.08942.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T06:01:41.924964Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T12:22:11.370210Z
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 ab59c0cb-0dad-483b-9c13-06434f0b647f · inbound
Neural Operators for Forward and Inverse Potential-Density Mappings in Classical Density Functional Theory cites this paper.
Neural Operators for Forward and Inverse Potential-Density Mappings in Classical Density Functional Theory Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T06:01:41.924964Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a474320f-456b-46a6-be8e-f6e3eb3b6179 · inbound
DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios cites this paper.
DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T10:20:47.271650Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ce6643a2-d8a1-40ee-b3e8-2b210902e568 · inbound
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks cites this paper.
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T12:22:11.472838Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.