Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2009.02484.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T05:41:44.238116Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T06:54:03.240968Z
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 20d56fa5-a80d-4b4e-80dd-cc387cf41bd4 · 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 Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T06:54:03.243985Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 89a5931a-39fe-4445-b79a-d1a8f919247d · inbound
Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion cites this paper.
Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities
Reference 1057
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T05:41:44.238116Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 521de22b-32fb-474a-bc8d-fbfc0a666e64 · inbound
Deep neural networks can provably solve Bellman equations for Markov decision processes without the curse of dimensionality cites this paper.
Deep neural networks can provably solve Bellman equations for Markov decision processes without the curse of dimensionality Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:06:59.435427Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d6cfc632-5d29-41aa-93fd-7bfca4eba260 · inbound
Full history recursive multilevel Picard approximations suffer from the curse of dimensionality for the Hamilton-Jacobi-Bellman equation of a stochastic control problem cites this paper.
Full history recursive multilevel Picard approximations suffer from the curse of dimensionality for the Hamilton-Jacobi-Bellman equation of a stochastic control problem Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:41:48.419144Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15c97d12-e630-4fa4-bbe5-4ffcda08a223 · inbound
Strong convergence and temporal-spatial regularity for tamed Euler approximations of L\'evy-driven SDEs cites this paper.
Strong convergence and temporal-spatial regularity for tamed Euler approximations of L\'evy-driven SDEs Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T22:56:33.382837Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.