Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:57:36.306720Z
Paper Citation Record · LEDGER
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning
As of 22 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2505.08497.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
13 of 13 outbound references displayed
- verified exact0
- verified fuzzy10
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 5c63cc2e-cc3c-4ab5-bae3-9f2ef6673a2b · outbound
This paper cites Hierarchical mixtures of experts and the em algorithm.Neural computation, 6(2):181–214, 1994.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning Hierarchical mixtures of experts and the em algorithm.Neural computation, 6(2):181–214, 1994
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:57:36.266543Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 525ff378-0c79-47c0-969d-ee6bffdaaf5a · outbound
This paper cites A domain decomposition method for fast manifold learning.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning A domain decomposition method for fast manifold learning
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.447070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 25bfd4f7-16d3-4982-aff1-476ff3cb69b8 · outbound
This paper cites Acceleration techniques for reduced-order models based on proper orthogonal decomposition.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning Acceleration techniques for reduced-order models based on proper orthogonal decomposition
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.436972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bb9293ec-f413-45e6-9b31-6e80d8bd902c · outbound
This paper cites Efficient domain decomposition for a neural network learning algorithm, used for the dose evaluation in external radiotherapy.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning Efficient domain decomposition for a neural network learning algorithm, used for the dose evaluation in external radiotherapy
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.426446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 76e8d0c6-8367-428c-aaf8-115f42f8de77 · outbound
This paper cites A training set and multiple bases generation approach for parameterized model reduction based on adaptive grids in parameter space.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning A training set and multiple bases generation approach for parameterized model reduction based on adaptive grids in parameter space
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.415881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 01a88c0d-c6cf-4ea1-b31c-e1a4d0109b62 · outbound
This paper cites Domain-decomposed bayesian inversion based on local karhunen-loève expansions.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning Domain-decomposed bayesian inversion based on local karhunen-loève expansions
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.404692Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5390f6cc-81ab-4a09-a001-bad0ee7277e1 · outbound
This paper cites Principal components analysis (pca).
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning Principal components analysis (pca)
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.394608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c5a08e3b-1b9a-499f-b03d-16fc9e0a1699 · outbound
This paper cites The moore–penrose pseudoinverse: A tutorial review of the theory.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning The moore–penrose pseudoinverse: A tutorial review of the theory
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:57:36.290876Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5368b5b8-77d8-4b1c-a6ec-7e6834733d89 · outbound
This paper cites The ball-pivoting algorithm for surface reconstruction.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning The ball-pivoting algorithm for surface reconstruction
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.378935Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8ade8baa-7a99-46cf-b70f-fff5074f666f · outbound
This paper cites Mathematical analysis of goldstein’s model for time-harmonic acoustics in flows.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning Mathematical analysis of goldstein’s model for time-harmonic acoustics in flows
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.368134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 34f5d09e-f6ee-4635-866e-7202406d0080 · outbound
This paper cites An adaptive sampling strategy for kriging metamodel based on delaunay triangulation and topsis.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning An adaptive sampling strategy for kriging metamodel based on delaunay triangulation and topsis
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.357933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 448c305d-5958-42b0-bb2f-ae42d2f4f17d · outbound
This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:57:36.303306Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9290f9fd-3ba3-4da6-9019-9844492b1996 · outbound
This paper cites https://www.irt-systemx.fr/projets/hsa/.
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning https://www.irt-systemx.fr/projets/hsa/
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T21:57:36.345980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.