Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.12355.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T21:55:52.810222Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T03:35:21.194065Z
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 17bee5b5-5b8f-4a90-ac64-a47402c72178 · inbound
BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics cites this paper.
BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T21:55:52.810222Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6ec6256-ce6f-4d9f-855f-3da779e7a971 · inbound
Neuro-Symbolic AI for Analytical Solutions of Differential Equations cites this paper.
Neuro-Symbolic AI for Analytical Solutions of Differential Equations Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T03:35:21.197306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 172cf004-823d-4b47-8c49-952d629c6b94 · inbound
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions cites this paper.
A Multimodal PDE Foundation Model for Prediction and Scientific Text Descriptions Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:02:12.826648Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03071def-2460-49a8-ba6b-9794ea69955b · inbound
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery cites this paper.
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:02:03.587920Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e0beff2c-21de-44ef-bc09-e95054563e9e · inbound
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations cites this paper.
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:33:52.869820Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.