Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:1808.01346.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T23:02:06.564553Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-02T08:06:47.698353Z
Reference resolution
0 of 0 outbound references displayed
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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 fd06cb36-d3c2-4b94-8e7d-6797e281cba7 · inbound
Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks cites this paper.
Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T23:02:06.564553Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dffbba91-cf2a-4b59-9291-3795835e2cfb · inbound
TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems cites this paper.
TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 33
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T07:32:42.797379Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation b05ab7c6-cc4c-4579-964b-dca37814cffb · inbound
Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics cites this paper.
Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T04:32:04.769936Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 769c853f-3b54-453b-9da7-ce4d1da8d469 · inbound
Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks cites this paper.
Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T19:53:49.345604Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf0efa45-523d-443b-a756-066cd4dd275b · inbound
Model reduction of parametric ordinary differential equations via autoencoders: representation properties and convergence analysis cites this paper.
Model reduction of parametric ordinary differential equations via autoencoders: representation properties and convergence analysis Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T13:56:26.594650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 54fc263e-9dd3-432e-85ce-ca6960c134e7 · inbound
Particle-Guided Diffusion Models for Partial Differential Equations cites this paper.
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 5ffe49b3-d89b-4a4c-af91-ba596966e4d2 · inbound
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach cites this paper.
Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T20:48:34.931596Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8ee65ae-7839-4e15-b91d-7cbffb5de570 · inbound
A Robust SINDy Autoencoder for Noisy Dynamical System Identification cites this paper.
A Robust SINDy Autoencoder for Noisy Dynamical System Identification Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T22:25:49.856659Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 2686af0c-d894-49a1-a328-399329ad4e72 · inbound
Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design cites this paper.
Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T13:20:26.710523Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 975f9541-b6d9-4d30-939f-f38b1e282dbe · inbound
One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators cites this paper.
One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T11:15:10.060496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation eb07cc2d-b469-46fb-b834-9713caa4f1b2 · inbound
Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry cites this paper.
Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T08:06:47.699490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation ebd2c381-3c38-410e-b8b7-a3d624b83405 · inbound
CASC: Causal Adversarial Subspace Clustering for Multivariate Spatiotemporal Data cites this paper.
CASC: Causal Adversarial Subspace Clustering for Multivariate Spatiotemporal Data Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T08:32:42.437276Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.