Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Space-time deep neural network approximations for high-dimensional partial differential equations
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2006.02199.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-24T12:37:24.021894Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T12:39:29.111904Z
Reference resolution
0 of 0 outbound references displayed
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- malformed identifier0
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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 bc220c65-f31c-4b80-bf0f-7f63771dc05f · inbound
Deep neural network approximation theory for high-dimensional functions cites this paper.
Deep neural network approximation theory for high-dimensional functions Space-time deep neural network approximations for high-dimensional partial differential equations
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T12:39:29.116103Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d26b59ab-4d13-4ceb-8771-96d4b418567e · inbound
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense cites this paper.
Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense Space-time deep neural network approximations for high-dimensional partial differential equations
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T06:54:03.251931Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.