Paper Citation Record · LEDGER

An overview on deep learning-based approximation methods for partial differential equations

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2012.12348.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2012.12348 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:08:03.386468Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T00:04:06.972461Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 1fb9d403-1534-4164-b3ff-ddcd2f262132 · inbound

Deep neural network approximation theory for high-dimensional functions cites this paper.

Deep neural network approximation theory for high-dimensional functions An overview on deep learning-based approximation methods for partial differential equations

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-24T12:39:29.051644Z

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 dbda77a9-c2ba-4a53-b059-136631b3b6be · inbound

Optimal Rebate Design: Incentives, Competition and Efficiency in Auction Markets cites this paper.

Optimal Rebate Design: Incentives, Competition and Efficiency in Auction Markets An overview on deep learning-based approximation methods for partial differential equations

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T17:08:03.386468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e132787-fd3a-4af8-806b-72f391b8e8dc · 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 An overview on deep learning-based approximation methods for partial differential equations

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T21:41:47.597395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 120fa588-97af-489b-8c14-3e4f37c0befd · inbound

Regulation or Competition:Major-Minor Optimal Liquidation across Dark and Lit Pools cites this paper.

Regulation or Competition:Major-Minor Optimal Liquidation across Dark and Lit Pools An overview on deep learning-based approximation methods for partial differential equations

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:18.416718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49536ee6-f95f-4e25-8136-31c565da73d3 · inbound

A Computational Method for Solving the Stochastic Joint Replenishment Problem in High Dimensions cites this paper.

A Computational Method for Solving the Stochastic Joint Replenishment Problem in High Dimensions An overview on deep learning-based approximation methods for partial differential equations

Reference 297

Resolution: unresolved; no resolver link, observed 2026-08-03T22:14:43.350901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89b63739-8db8-491b-8680-54cc98dc3b33 · inbound

Stochastic Transition-Map Distillation for Fast Probabilistic Inference cites this paper.

Stochastic Transition-Map Distillation for Fast Probabilistic Inference An overview on deep learning-based approximation methods for partial differential equations

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:25:53.436057Z

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 6fc69c29-8a4e-4cb3-8eaa-c7766e78de14 · inbound

Random Neural Network Expressivity for Non-Linear Partial Differential Equations cites this paper.

Random Neural Network Expressivity for Non-Linear Partial Differential Equations An overview on deep learning-based approximation methods for partial differential equations

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-30T00:04:06.973912Z

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 64ca3141-9710-4dcb-80be-feb831c82555 · inbound

Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers cites this paper.

Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers An overview on deep learning-based approximation methods for partial differential equations

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-01T06:09:00.997699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.