Paper Citation Record · LEDGER

Mixture of neural operator experts for learning boundary conditions and model selection

As of 11 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 2 inbound Pith citation observations for arXiv:2502.04562.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.04562 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:25:11.273722Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T08:35:30.948277Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T06:31:00.870804Z

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy14
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 916e31c8-29c4-423f-9f41-4d30d2afc30e · outbound

This paper cites an unresolved cited work.

Mixture of neural operator experts for learning boundary conditions and model selection Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.132436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b2b413b-2f4c-43fb-8ebe-099ff06e90b5 · outbound

This paper cites Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems.

Mixture of neural operator experts for learning boundary conditions and model selection Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.137757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edb3a5fe-c420-4251-b3ed-ce51e48dcd57 · outbound

This paper cites Nonlinear integro-differential operator regression with neural networks.

Mixture of neural operator experts for learning boundary conditions and model selection Nonlinear integro-differential operator regression with neural networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.143041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9a9814f-776c-47fb-9c1d-cf41db995be2 · outbound

This paper cites Patel, Nathaniel A.

Mixture of neural operator experts for learning boundary conditions and model selection Patel, Nathaniel A

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:12.043853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e35536d8-7c0d-47ea-9240-48307586e605 · outbound

This paper cites Fourier neural operator for parametric partial differential equations.

Mixture of neural operator experts for learning boundary conditions and model selection Fourier neural operator for parametric partial differential equations

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:12.029191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e529c849-e92d-4ff9-837f-6a10e5ec2de2 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.

Mixture of neural operator experts for learning boundary conditions and model selection Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.157648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c41bcee-84e1-4f4f-8e7f-a6c317ae694a · outbound

This paper cites U- NO : U-shaped neural operators.

Mixture of neural operator experts for learning boundary conditions and model selection U- NO : U-shaped neural operators

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:12.013632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 04bf3c89-d4d4-4602-83d2-13fe6158e0e3 · outbound

This paper cites Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems.

Mixture of neural operator experts for learning boundary conditions and model selection Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems

Reference 8

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T22:25:11.754323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c3c2e4fa-083a-4f8a-9141-37c8968fc3ac · outbound

This paper cites A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data.

Mixture of neural operator experts for learning boundary conditions and model selection A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.996384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d4a00df2-451d-4de9-be8f-986793fbb126 · outbound

This paper cites Learning neural operators on riemannian manifolds.

Mixture of neural operator experts for learning boundary conditions and model selection Learning neural operators on riemannian manifolds

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.177339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60496cd9-7854-44f9-841d-99e1b511d0b3 · outbound

This paper cites Fourier neural operator with learned deformations for pdes on general geometries.

Mixture of neural operator experts for learning boundary conditions and model selection Fourier neural operator with learned deformations for pdes on general geometries

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.981416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f6edcc14-c669-476a-b6fa-e17920d03e1f · outbound

This paper cites Partition of unity networks: deep hp-approximation.

Mixture of neural operator experts for learning boundary conditions and model selection Partition of unity networks: deep hp-approximation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.186562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f63b2292-3f65-4aa0-b2a3-26fb0a426d5d · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Mixture of neural operator experts for learning boundary conditions and model selection Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.191674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5716c137-ddb4-4fbe-9849-0b07b95cc0f8 · outbound

This paper cites A public turbulence database cluster and applications to study lagrangian evolution of velocity increments in turbulence.

Mixture of neural operator experts for learning boundary conditions and model selection A public turbulence database cluster and applications to study lagrangian evolution of velocity increments in turbulence

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.966499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4be38288-20ba-40cc-a93b-6641d54003ea · outbound

This paper cites A web services accessible database of turbulent channel flow and its use for testing a new integral wall model for les.

Mixture of neural operator experts for learning boundary conditions and model selection A web services accessible database of turbulent channel flow and its use for testing a new integral wall model for les

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.951135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3203ba9e-5cdf-490e-89f7-c80614e01e42 · outbound

This paper cites Data exploration of turbulence simulations using a database cluster.

Mixture of neural operator experts for learning boundary conditions and model selection Data exploration of turbulence simulations using a database cluster

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.936033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8e1ef99f-d798-438d-8916-f82a265e898b · outbound

This paper cites Variational inference: A review for statisticians.

Mixture of neural operator experts for learning boundary conditions and model selection Variational inference: A review for statisticians

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.210839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e766bbb1-5a32-44ab-9600-473a523485bc · outbound

This paper cites Prediction of turbulent channel flow using fourier neural operator-based machine-learning strategy.

Mixture of neural operator experts for learning boundary conditions and model selection Prediction of turbulent channel flow using fourier neural operator-based machine-learning strategy

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.909589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 25bd2d0f-ae9a-4689-80ca-da424d446d1f · outbound

This paper cites A characteristic based volume penalization method for general evolution problems applied to compressible viscous flows.

Mixture of neural operator experts for learning boundary conditions and model selection A characteristic based volume penalization method for general evolution problems applied to compressible viscous flows

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.893819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7216bce-1e83-4e13-9bd7-6e78a0bed55e · outbound

This paper cites A volume penalization method for incompressible flows and scalar advection--diffusion with moving obstacles.

Mixture of neural operator experts for learning boundary conditions and model selection A volume penalization method for incompressible flows and scalar advection--diffusion with moving obstacles

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.878208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5faa83b6-1dcf-4514-9190-ccd941a9cce9 · outbound

This paper cites Numerical simulation of fluid--structure interaction with the volume penalization method.

Mixture of neural operator experts for learning boundary conditions and model selection Numerical simulation of fluid--structure interaction with the volume penalization method

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.862388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ceee7fbf-20e8-46f6-924d-56df6f0bb9a0 · outbound

This paper cites A fourier spectral method for the navier--stokes equations with volume penalization for moving solid obstacles.

Mixture of neural operator experts for learning boundary conditions and model selection A fourier spectral method for the navier--stokes equations with volume penalization for moving solid obstacles

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.846751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5f1480f6-d530-440e-87ee-e71fd6dde9d4 · outbound

This paper cites Constrained optimization and Lagrange multiplier methods.

Mixture of neural operator experts for learning boundary conditions and model selection Constrained optimization and Lagrange multiplier methods

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.240170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e68052c3-8436-4e83-abea-5a219f47478a · outbound

This paper cites Auto-Encoding Variational Bayes.

Mixture of neural operator experts for learning boundary conditions and model selection Auto-Encoding Variational Bayes

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.244817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4c6941c-3f49-44be-99eb-7a850811800e · outbound

This paper cites Weight uncertainty in neural network.

Mixture of neural operator experts for learning boundary conditions and model selection Weight uncertainty in neural network

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.249968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 671dd896-511c-4c14-82a7-b4f2ad7da935 · outbound

This paper cites What are bayesian neural network posteriors really like? In International conference on machine learning, pages 4629--4640.

Mixture of neural operator experts for learning boundary conditions and model selection What are bayesian neural network posteriors really like? In International conference on machine learning, pages 4629--4640

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T22:25:11.809732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d624f376-44a0-4e63-a64b-555cc136f2e6 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Mixture of neural operator experts for learning boundary conditions and model selection PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.259488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0f8feab-5a0a-464b-86d9-f8ec9bdaf5ab · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Mixture of neural operator experts for learning boundary conditions and model selection Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.264519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6234f62c-6540-4f0e-acd5-667b7ba76966 · outbound

This paper cites Super-convergence: Very fast training of neural networks using large learning rates.

Mixture of neural operator experts for learning boundary conditions and model selection Super-convergence: Very fast training of neural networks using large learning rates

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.269355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89a5cfcf-1f8a-437b-9484-adf601ed0877 · outbound

This paper cites Scheduled sampling for sequence prediction with recurrent neural networks.

Mixture of neural operator experts for learning boundary conditions and model selection Scheduled sampling for sequence prediction with recurrent neural networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-08T22:25:11.273722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation d7a61a88-e2d6-4028-abfa-d66739384b22 · inbound

SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion cites this paper.

SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion Mixture of neural operator experts for learning boundary conditions and model selection

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:31:00.874568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 276bcdb4-8d67-41ec-a3cc-c31a46b66988 · inbound

SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion cites this paper.

SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion Mixture of neural operator experts for learning boundary conditions and model selection

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-13T08:35:30.948277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.