Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T16:35:15.199034Z
Paper Citation Record · LEDGER
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems
As of 15 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2512.13163.
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 2026-08-15T06:32:42.880941+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 exact1
- verified fuzzy0
- unresolved12
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 83230be2-31ca-494b-a33b-2845fae89ff9 · outbound
This paper cites Alvarez, Lorenzo Rosasco, and Neil D.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Alvarez, Lorenzo Rosasco, and Neil D
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:13.439922Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ef99f70-69e7-49ea-8f12-8d1fe8606dbc · outbound
This paper cites Kernel methods are competitive for operator learning.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Kernel methods are competitive for operator learning
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:13.618182Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30e45104-f5b9-46a8-bb48-9f456845ddf5 · outbound
This paper cites Gaussian process port- H amiltonian systems : Bayesian learning with physics prior.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Gaussian process port- H amiltonian systems : Bayesian learning with physics prior
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:13.841275Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eabc40eb-035d-4aa0-8c90-7775a6071951 · outbound
This paper cites Partitioned finite element method for power-preserving discretization of open systems of conservation laws.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Partitioned finite element method for power-preserving discretization of open systems of conservation laws
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:14.043733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42255016-7a11-46f1-b95c-cdee4392f4a5 · outbound
This paper cites Simulation and control of interactions in multi-physics, a python package for port-hamiltonian systems.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Simulation and control of interactions in multi-physics, a python package for port-hamiltonian systems
Reference 5
- Resolution
- verified exact
- doi, observed 2026-08-03T16:38:31.553633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 12d695cb-afc2-4663-ad4d-6e2bca694ca7 · outbound
This paper cites NAPI-MPC : Neural accelerated physics-informed MPC for nonlinear PDE systems.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems NAPI-MPC : Neural accelerated physics-informed MPC for nonlinear PDE systems
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:14.304678Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a66c4b6a-12ea-467f-8764-494399886e3f · outbound
This paper cites Early-and late-lumping observer designs for long hydraulic pipelines: Application to pumped-storage power plants.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Early-and late-lumping observer designs for long hydraulic pipelines: Application to pumped-storage power plants
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:14.418536Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51e17b89-493f-4d4a-9dd7-78cf393e393e · outbound
This paper cites Physics-guided transfer learning for B ayesian optimization of chemical port- H amiltonian systems.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Physics-guided transfer learning for B ayesian optimization of chemical port- H amiltonian systems
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:14.530964Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 42377bd9-63cd-40ca-a470-108f13d23eea · outbound
This paper cites Gaussian Processes for Machine Learning.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Gaussian Processes for Machine Learning
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:14.684700Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0836688d-eca7-4cee-bb79-3d2b86cd882f · outbound
This paper cites Metamodeling with G aussian processes.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Metamodeling with G aussian processes
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:14.823922Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0b293db9-3b4a-406c-b152-af8741316cca · outbound
This paper cites Physics-constrained learning of PDE systems with uncertainty quantified port- H amiltonian models.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Physics-constrained learning of PDE systems with uncertainty quantified port- H amiltonian models
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:14.952810Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54cde6eb-2ad4-48e7-91bc-11d6e9d39505 · outbound
This paper cites Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Plug-and-Play Physics-informed Learning using Uncertainty Quantified Port-Hamiltonian Models
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:15.072744Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ce893c5-933a-4330-b807-94d1cfe9d4d7 · outbound
This paper cites Hamiltonian formulation of distributed-parameter systems with boundary energy flow.
PFEM-GP-dPHS : a finite element framework for combining Gaussian processes and infinite-dimensional port-Hamiltonian systems Hamiltonian formulation of distributed-parameter systems with boundary energy flow
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T16:35:15.199034Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.