Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T06:13:17.727600Z
Paper Citation Record · LEDGER
Particle-Guided Diffusion Models for Partial Differential Equations
As of 9 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2601.23262.
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 2026-07-13T14:19:58.645410Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-13T22:23:21.473283Z
Reference resolution
12 of 12 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved10
- parse uncertain0
- malformed identifier2
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 54fc263e-9dd3-432e-85ce-ca6960c134e7 · outbound
This paper cites Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems.
Particle-Guided Diffusion Models for Partial Differential Equations Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:16.380943Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9979f988-a49b-428e-a55d-859ed1ca5bbd · outbound
This paper cites In pBS, each particle receives an incremental weight Gj−1(xj, xj−1) = ˜pθ(y|x j−1) ˜pθ(y|x j).
Particle-Guided Diffusion Models for Partial Differential Equations In pBS, each particle receives an incremental weight Gj−1(xj, xj−1) = ˜pθ(y|x j−1) ˜pθ(y|x j)
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:17.643650Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1acbd8ff-b3c4-4f80-9d65-1f93dfc4ad89 · outbound
This paper cites Huang, R., Huang, J., Yang, D., Ren, Y ., Liu, L., Li, M., Ye, Z., Liu, J., Yin, X., and Zhao, Z.
Particle-Guided Diffusion Models for Partial Differential Equations Huang, R., Huang, J., Yang, D., Ren, Y ., Liu, L., Li, M., Ye, Z., Liu, J., Yin, X., and Zhao, Z
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:16.532968Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d72af2e-c4ca-4e05-8d3e-8bd38a5d8368 · outbound
This paper cites Generative Latent Neural PDE Solver using Flow Matching.
Particle-Guided Diffusion Models for Partial Differential Equations Generative Latent Neural PDE Solver using Flow Matching
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:16.817315Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6fb44882-b737-4273-84a0-f036b46cc3d1 · outbound
This paper cites A Denoising Diffusion Model for Fluid Field Prediction.
Particle-Guided Diffusion Models for Partial Differential Equations A Denoising Diffusion Model for Fluid Field Prediction
Reference 9
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-03T06:13:17.308364Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a0cd04f9-db97-4709-9b9a-f0668401ae3f · outbound
This paper cites In pBS, each particle is propagated via the guided sampler x(i) j−1 ∼M j−1(· |xj) = ˜pθ(xj−1 |x j, y).
Particle-Guided Diffusion Models for Partial Differential Equations In pBS, each particle is propagated via the guided sampler x(i) j−1 ∼M j−1(· |xj) = ˜pθ(xj−1 |x j, y)
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:17.464538Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e85d9bc6-5ae4-4dc3-bfd7-7e03c4406a0a · outbound
This paper cites In SMC, when the effective sample size drops below Neff, particles are resampled with probability proportional to their normalised weights.
Particle-Guided Diffusion Models for Partial Differential Equations In SMC, when the effective sample size drops below Neff, particles are resampled with probability proportional to their normalised weights
Reference 12
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-03T06:13:17.727600Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88a50d95-3e2e-4dfa-91ae-17664761aa37 · outbound
This paper cites Shysheya, A., Diaconu, C., Bergamin, F., Perdikaris, P., Hern´andez-Lobato, J.
Particle-Guided Diffusion Models for Partial Differential Equations Shysheya, A., Diaconu, C., Bergamin, F., Perdikaris, P., Hern´andez-Lobato, J
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:17.047854Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8b6b4fbb-2c15-48cd-99a5-d3a31b799bd3 · outbound
This paper cites Stevens, T.
Particle-Guided Diffusion Models for Partial Differential Equations Stevens, T
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:17.189608Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e50f3276-83cc-4621-9cfc-06a4a60c94f3 · outbound
This paper cites Kelvinius, F.
Particle-Guided Diffusion Models for Partial Differential Equations Kelvinius, F
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:16.683513Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 29271ae9-8b52-4ca1-95ff-4bdb0b212513 · outbound
This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.
Particle-Guided Diffusion Models for Partial Differential Equations DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:16.940051Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f86ad6fa-fc01-4521-ba43-5b8c98020019 · outbound
This paper cites Diffusion models for inverse problems.
Particle-Guided Diffusion Models for Partial Differential Equations Diffusion models for inverse problems
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T06:13:16.293605Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 0670f053-9743-45eb-add3-de9de1940f87 · inbound
Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing cites this paper.
Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing Particle-Guided Diffusion Models for Partial Differential Equations
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-28T03:04:45.314611Z
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 f8f20e0c-78cf-4e08-8039-cd1446062f66 · inbound
Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing cites this paper.
Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing Particle-Guided Diffusion Models for Partial Differential Equations
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T14:19:58.645410Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.