Paper Citation Record · LEDGER

Domain decomposition of large neural network surrogate models

As of 15 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2603.26396.

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

pith.paper-citation-record.v1
2603.26396 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T17:20:12.066401Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

30 of 30 outbound references displayed

verified exact4
verified fuzzy0
unresolved21
parse uncertain0
malformed identifier5
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca321408-db75-42ab-aa63-49d042b7f190 · outbound

This paper cites Robust optimization – A comprehensive survey.

Domain decomposition of large neural network surrogate models Robust optimization – A comprehensive survey

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:11.980649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce2c2bd8-8dc5-414a-8429-891f22035ecb · outbound

This paper cites Chan.Theory and Applications of Monte Carlo Simulations.

Domain decomposition of large neural network surrogate models Chan.Theory and Applications of Monte Carlo Simulations

Reference 2

Resolution: malformed identifier; no resolver link, observed 2026-08-02T17:20:11.985439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83248636-8d19-45df-b163-4d2801f0d715 · outbound

This paper cites Forrester, A.

Domain decomposition of large neural network surrogate models Forrester, A

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:11.988475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d30b61ad-779b-4bb8-9579-dd642de27984 · outbound

This paper cites The Homogeneous Chaos.

Domain decomposition of large neural network surrogate models The Homogeneous Chaos

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:11.991733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e207725b-4241-4b51-9e3b-e8e142ad38b5 · outbound

This paper cites Weighted discrete least-squares polynomial approximation using randomized quadratures.

Domain decomposition of large neural network surrogate models Weighted discrete least-squares polynomial approximation using randomized quadratures

Reference 5

Resolution: verified exact; doi, observed 2026-08-02T17:24:16.458805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a0c78332-55aa-4cde-b5e6-eb4adc9bcd3a · outbound

This paper cites Universal approximation bounds for superpositions of a sigmoidal function.

Domain decomposition of large neural network surrogate models Universal approximation bounds for superpositions of a sigmoidal function

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:11.997038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0130ed0c-bc96-4b59-9030-38853d45e307 · outbound

This paper cites Toselli and O.

Domain decomposition of large neural network surrogate models Toselli and O

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:11.999844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c5d48a3-3ba5-41f2-9eef-87ee8d6160bc · outbound

This paper cites ¨Uber einige Abbildungsaufgaben.

Domain decomposition of large neural network surrogate models ¨Uber einige Abbildungsaufgaben

Reference 8

Resolution: verified exact; doi, observed 2026-08-02T17:24:16.187457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 64b1c882-f0d6-466f-aa9b-f04d36aae683 · outbound

This paper cites On the Schwarz Alternating Method I.

Domain decomposition of large neural network surrogate models On the Schwarz Alternating Method I

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.005092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9139a54-c247-4a49-9418-3e4543418715 · outbound

This paper cites On the Schwarz Alternating Method III: A Variant for Nonoverlapping Subdomains.

Domain decomposition of large neural network surrogate models On the Schwarz Alternating Method III: A Variant for Nonoverlapping Subdomains

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.007452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6aa9f05-0159-4366-808a-58b1fe87897b · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equa- tions.

Domain decomposition of large neural network surrogate models Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equa- tions

Reference 11

Resolution: malformed identifier; no resolver link, observed 2026-08-02T17:20:12.009698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee6df107-187e-4792-b440-81aa7d13b023 · outbound

This paper cites Domain Decomposition Algorithms for Neural Network Approximation of Partial Differential Equations.

Domain decomposition of large neural network surrogate models Domain Decomposition Algorithms for Neural Network Approximation of Partial Differential Equations

Reference 12

Resolution: malformed identifier; doi_truncated, observed 2026-08-02T17:24:16.033239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 37771c96-b8fd-4155-b2f7-24e70bbaf5b3 · outbound

This paper cites D3M: A Deep Domain Decomposition Method for Partial Differential Equations.

Domain decomposition of large neural network surrogate models D3M: A Deep Domain Decomposition Method for Partial Differential Equations

Reference 13

Resolution: malformed identifier; no resolver link, observed 2026-08-02T17:20:12.019738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecd30217-da35-4be0-a818-e9943a48bd91 · outbound

This paper cites Deep Domain Decomposition Method: Elliptic Problems.

Domain decomposition of large neural network surrogate models Deep Domain Decomposition Method: Elliptic Problems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.022507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a762dfc-91cd-4f49-9d60-eab62928ccd4 · outbound

This paper cites Deep Ritz method with adaptive quadrature for linear elasticity.

Domain decomposition of large neural network surrogate models Deep Ritz method with adaptive quadrature for linear elasticity

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.025374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 757a8688-c2b5-4e59-bdba-353aa5d8ce94 · outbound

This paper cites Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations.

Domain decomposition of large neural network surrogate models Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.028075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84dd47a3-b37b-4e8c-b206-b39269379065 · outbound

This paper cites Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations.

Domain decomposition of large neural network surrogate models Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

Reference 17

Resolution: malformed identifier; no resolver link, observed 2026-08-02T17:20:12.030653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfde942e-d629-4925-8b47-a221a14b7fd7 · outbound

This paper cites Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems.

Domain decomposition of large neural network surrogate models Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.032885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 635e819e-592f-4f3d-99f7-8ffa9b3b8127 · outbound

This paper cites Partitioned neural network approximation for partial differential equations enhanced with Lagrange multipliers and localized loss functions.

Domain decomposition of large neural network surrogate models Partitioned neural network approximation for partial differential equations enhanced with Lagrange multipliers and localized loss functions

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.035696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ced5f52-fbf0-4ca9-96d6-96defb5df7f1 · outbound

This paper cites Compact Operators. Spectral Decomposition of Self-Adjoint Compact Operators.

Domain decomposition of large neural network surrogate models Compact Operators. Spectral Decomposition of Self-Adjoint Compact Operators

Reference 20

Resolution: verified exact; doi, observed 2026-08-02T17:24:15.805415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a9070890-5d7b-40f4-9836-fa48170856db · outbound

This paper cites Variational formulations and functional approximation algorithms in stochastic plas- ticity of materials.

Domain decomposition of large neural network surrogate models Variational formulations and functional approximation algorithms in stochastic plas- ticity of materials

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.041917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fc16ce8-8190-4ffd-ab26-387e9f66ab82 · outbound

This paper cites Bathe.Finite Element Procedures in Engineering Analysis.

Domain decomposition of large neural network surrogate models Bathe.Finite Element Procedures in Engineering Analysis

Reference 22

Resolution: verified exact; doi, observed 2026-08-02T17:24:15.534522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cc4e32ac-37b3-4b63-a451-4aac71213a11 · outbound

This paper cites Nocedal and S.

Domain decomposition of large neural network surrogate models Nocedal and S

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.046966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75ccbe68-8e4c-451e-a008-0d35c8ce3018 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learn- ing Library.

Domain decomposition of large neural network surrogate models PyTorch: An Imperative Style, High-Performance Deep Learn- ing Library

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.049401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4811f4e8-693a-4322-88ac-bad413b852a6 · outbound

This paper cites Abadi, A.

Domain decomposition of large neural network surrogate models Abadi, A

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.052220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41cb4fc6-0d4e-4d07-8339-3188c7a980a3 · outbound

This paper cites Distributed optimization and statisti- cal learning via the alternating direction method of multipliers.

Domain decomposition of large neural network surrogate models Distributed optimization and statisti- cal learning via the alternating direction method of multipliers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.054784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cf8e05c-1b36-4a50-bb2d-d436155fbbb8 · outbound

This paper cites Lagrange Multipliers Revisited.

Domain decomposition of large neural network surrogate models Lagrange Multipliers Revisited

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.057553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4ecfa7a-344a-46aa-ae1d-88fad8d5fd29 · outbound

This paper cites On the Problem of Local Minima in Backpropagation.

Domain decomposition of large neural network surrogate models On the Problem of Local Minima in Backpropagation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.059952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0732690-8457-4b54-8f46-ec4216c595b4 · outbound

This paper cites A Limited Memory Algorithm for Bound Constrained Optimization.

Domain decomposition of large neural network surrogate models A Limited Memory Algorithm for Bound Constrained Optimization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.063371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23af0d24-d0b9-4c9b-99a0-755045692af8 · outbound

This paper cites An overview of gradient descent optimization algorithms.

Domain decomposition of large neural network surrogate models An overview of gradient descent optimization algorithms

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T17:20:12.066401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.