Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2004.01806.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:50:50.405698Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T07:59:40.360688Z
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 6e9c2167-9dd0-420d-a6d8-8e46db542120 · inbound
XNet-Enhanced Deep BSDE Method and Numerical Analysis cites this paper.
XNet-Enhanced Deep BSDE Method and Numerical Analysis On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T04:22:31.121277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 58503be7-cad5-44d0-8021-92b65da03543 · inbound
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations cites this paper.
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:50:50.405698Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 637ddf3e-3659-4deb-b778-c5efe0cf5706 · inbound
S-shaped Utility Maximization with VaR Constraint and Partial Information cites this paper.
S-shaped Utility Maximization with VaR Constraint and Partial Information On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:59:31.606230Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 319bc03a-a1cc-4dd8-b8d0-0ebf4d9b1e63 · inbound
Structure-Informed Deep Reinforcement Learning for Inventory Management cites this paper.
Structure-Informed Deep Reinforcement Learning for Inventory Management On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T12:12:46.234443Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 23b32805-c502-4211-a89d-9f6e7077ccba · inbound
A Practitioner's Guide to Kolmogorov-Arnold Networks cites this paper.
A Practitioner's Guide to Kolmogorov-Arnold Networks On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T03:30:50.478444Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 42e99c5f-d11b-4763-9623-1c526a3df420 · inbound
Universal Approximation of Nonlinear Operators and Their Derivatives cites this paper.
Universal Approximation of Nonlinear Operators and Their Derivatives On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 115
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T16:52:40.272497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 96815386-8b1c-4db6-a706-db6ccd34c977 · inbound
Universal Approximation of Nonlinear Operators and Their Derivatives cites this paper.
Universal Approximation of Nonlinear Operators and Their Derivatives On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 115
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T20:55:04.282366Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 32d502e6-e918-48cd-bd75-60fa49193d2f · inbound
PINNsur: Physics-Informed Neural Networks for PDEs on Curved Surfaces cites this paper.
PINNsur: Physics-Informed Neural Networks for PDEs on Curved Surfaces On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T14:33:30.493388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 83d8b2fc-f221-439c-b303-5d53e05f3c0d · inbound
Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks cites this paper.
Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T15:47:06.082524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9dac4652-bc26-4ea5-ae50-c6df012c2f38 · inbound
Error Analysis of Tr-PINNs Algorithm for 2D Incompressible Navier-Stokes Equations with Non-Homogeneous Boundary Conditions cites this paper.
Error Analysis of Tr-PINNs Algorithm for 2D Incompressible Navier-Stokes Equations with Non-Homogeneous Boundary Conditions On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T14:37:04.160153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation aac4c5c0-fdd3-4450-9850-8dcaa70a8708 · inbound
Parameterized Representations via Implicit Stochastic Modulation for High-Dimensional and High-Order Neural PDE Solvers cites this paper.
Parameterized Representations via Implicit Stochastic Modulation for High-Dimensional and High-Order Neural PDE Solvers On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T07:59:40.362054Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 60b0783b-a236-4d2c-ae5e-44359c22f354 · inbound
Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder cites this paper.
Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-07-11T09:54:49.262236Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.