Paper Citation Record · LEDGER

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios

As of 14 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 1 inbound Pith citation observation for arXiv:2508.02717.

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

pith.paper-citation-record.v1
2508.02717 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:20:47.303644Z

measured 60 of 60 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T01:01:47.459854Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T15:45:48.818426Z

Reference resolution

59 of 59 outbound references displayed

verified exact0
verified fuzzy52
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02c3dd5c-1499-4f53-bd74-f25a3a0fba72 · outbound

This paper cites Zaky, Ahmed S.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Zaky, Ahmed S

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:48.042275Z

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 44667d18-65cb-4803-83c3-8cf6f9ddcbee · outbound

This paper cites Computational Methods for Electromagnetic Phenomena: Electrostatics in Solvation, Scattering, and Electron Transport.Cambridge University Press, 2013.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Computational Methods for Electromagnetic Phenomena: Electrostatics in Solvation, Scattering, and Electron Transport.Cambridge University Press, 2013

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:48.030889Z

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 3488665a-a235-4bc9-a6f4-dfdfc64c2137 · outbound

This paper cites Parallel modeling of cell suspension flow in complex micro-networks with inflow/outflow boundary conditions.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Parallel modeling of cell suspension flow in complex micro-networks with inflow/outflow boundary conditions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:48.019952Z

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 f7c94279-8a92-4872-86db-f6b7d83be88a · outbound

This paper cites Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations.Comput.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations.Comput

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:48.008519Z

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 2a9dd462-5683-4b65-bc6a-3d4d744225f1 · outbound

This paper cites Fuhg and Nikolaos Bouklas.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Fuhg and Nikolaos Bouklas

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.997005Z

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 da11ee08-8bf4-4fad-b8ae-2c4bfd632718 · outbound

This paper cites Finite element methods for the viscous incompressible fluid.Appl.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Finite element methods for the viscous incompressible fluid.Appl

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.985597Z

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 9557692f-4403-4c1f-b17e-93cfc625a86d · outbound

This paper cites an unresolved cited work.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-06T10:20:47.973368Z

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 d61ee45c-671f-469f-b9ef-82d20d65b89c · outbound

This paper cites Gray and N.M.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Gray and N.M

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.962056Z

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 bc2ec340-4caa-4974-8305-d5ca0d766a01 · outbound

This paper cites an unresolved cited work.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-06T10:20:47.949029Z

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 881d161e-90c0-46cc-af35-7cfe792cbe84 · outbound

This paper cites Classicalandreactivemoleculardynamics:Principles and applications in combustion and energy systems.Prog.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Classicalandreactivemoleculardynamics:Principles and applications in combustion and energy systems.Prog

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.936871Z

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 a65319e3-9976-48f4-a061-ac5c376d7185 · outbound

This paper cites an unresolved cited work.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-06T10:20:47.926059Z

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 d77ec755-8ab6-464c-ba69-00a23aa1f06b · outbound

This paper cites Spectral Methods: Algorithms, Analysis and Applications.SpringerPublishingCompany,Incorporated, 1st edition, 2011.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Spectral Methods: Algorithms, Analysis and Applications.SpringerPublishingCompany,Incorporated, 1st edition, 2011

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.914801Z

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 8afc062f-f910-44da-a7fb-bd773856afeb · outbound

This paper cites Iterative Methods for Sparse Linear Systems.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Iterative Methods for Sparse Linear Systems

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.903593Z

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 c5f69a2b-3799-4bca-9206-0f126fd6f70a · outbound

This paper cites Widlund.Domain decomposition methods-algorithms and theory.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Widlund.Domain decomposition methods-algorithms and theory

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.892715Z

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 b554928b-e34c-4b07-b5be-3a48d28e4b36 · outbound

This paper cites Lecun, L.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Lecun, L

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.880740Z

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 fef6045f-980b-497d-bdb0-b2ab1688cf2a · outbound

This paper cites Attention is all you need.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Attention is all you need

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.869290Z

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 ea9d5b5d-8e90-44c1-ae57-018577a3c19a · outbound

This paper cites Dgm: A deep learning algorithm for solving partial differential equations.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Dgm: A deep learning algorithm for solving partial differential equations.J

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.856079Z

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 568ca5b2-d8fc-40a4-9b29-6faa97a594e2 · outbound

This paper cites Thedeepritzmethod:Adeeplearning-basednumericalalgorithmforsolvingvariationalproblems.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Thedeepritzmethod:Adeeplearning-basednumericalalgorithmforsolvingvariationalproblems

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.842178Z

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 3aeb1b91-6960-46f0-83f9-bf32198d5b05 · outbound

This paper cites Physics-informedneuralnetworks:Adeeplearningframeworkforsolvingforwardandinverse problems involving nonlinear partial differential equations.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Physics-informedneuralnetworks:Adeeplearningframeworkforsolvingforwardandinverse problems involving nonlinear partial differential equations.J

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.830593Z

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 e31f8170-142a-4c56-ba09-769089ba7e74 · outbound

This paper cites Neural operator: Learning maps between function spaces with applications to pdes.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Neural operator: Learning maps between function spaces with applications to pdes.J

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.819339Z

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 23bd6d90-06aa-4bb0-a106-6359568c3b7c · outbound

This paper cites Ascalableframeworkforlearningthegeometry- dependent solution operators of partial differential equations.Nat.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Ascalableframeworkforlearningthegeometry- dependent solution operators of partial differential equations.Nat

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.806508Z

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 51cc6bca-7d79-4bc2-8411-f60d78efb44f · outbound

This paper cites Geom-deeponet: A point-cloud-based deep operator network for field predictions on 3d parameterized geometries.Comput.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Geom-deeponet: A point-cloud-based deep operator network for field predictions on 3d parameterized geometries.Comput

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.794052Z

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 42c44bdc-d0e9-44b0-97f4-210481bee790 · outbound

This paper cites Multilayer feedforward networks are universal approximators.Neural Netw., 2(5):359–366, 1989.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Multilayer feedforward networks are universal approximators.Neural Netw., 2(5):359–366, 1989

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.780540Z

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 48c5c8a4-19b0-4362-9b29-a0a544b617cc · outbound

This paper cites Universalapproximationtononlinearoperatorsbyneuralnetworkswitharbitraryactivationfunctionsandits application to dynamical systems.IEEE Trans.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Universalapproximationtononlinearoperatorsbyneuralnetworkswitharbitraryactivationfunctionsandits application to dynamical systems.IEEE Trans

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.768517Z

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 ee32f4b1-d8ed-40c6-8c47-0e9175bef21a · outbound

This paper cites Nonlocal kernel network (nkn): A stable and resolution-independent deep neural network.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Nonlocal kernel network (nkn): A stable and resolution-independent deep neural network.J

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.756219Z

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 98453897-e6cd-4150-94e2-15300e431284 · outbound

This paper cites Koopman neural operator as a mesh-free solver of non-linear partial differential equations.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Koopman neural operator as a mesh-free solver of non-linear partial differential equations.J

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.738404Z

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 817eeffe-c33d-4ea9-85c3-9d07e78e068f · outbound

This paper cites Seidman, Leonardo Ferreira Guilhoto, Victor M.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Seidman, Leonardo Ferreira Guilhoto, Victor M

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.716535Z

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 177c8f17-d507-4f3e-a221-c9683c748901 · outbound

This paper cites Fourierneuraloperatorforparametricpartialdifferentialequations.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Fourierneuraloperatorforparametricpartialdifferentialequations

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.703583Z

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 17f4cb58-9285-4e52-a4c6-5358b20b6712 · outbound

This paper cites Learningnonlinearoperatorsviadeeponetbasedonthe universal approximation theorem of operators.Nat.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Learningnonlinearoperatorsviadeeponetbasedonthe universal approximation theorem of operators.Nat

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.685313Z

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 5944c23a-5c08-4fc4-a852-6c1fc677157f · outbound

This paper cites Kevrekidis, and Michael D.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Kevrekidis, and Michael D

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.672919Z

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 61e93bc7-2a31-4113-82ad-6632e88850f9 · outbound

This paper cites Phygeonet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state pdes on irregular domain.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Phygeonet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state pdes on irregular domain.J

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.658928Z

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 adc6d6bf-b12c-43d9-bd11-209a3b57437c · outbound

This paper cites Three operator learning models for solving boundary integral equations in 2d connected domains.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Three operator learning models for solving boundary integral equations in 2d connected domains

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.646555Z

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 2b8eae2e-1dc4-463d-a75e-01353dd58994 · outbound

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

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Fourier neural operator with learned deformations for pdes on general geometries.J

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:47.204632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 862a5223-0a74-4cd7-a7e7-01c99a0e6e9e · outbound

This paper cites Learning solution operators of PDEs defined on varying domains via MIONet.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Learning solution operators of PDEs defined on varying domains via MIONet

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:47.208129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d385fdc0-30a1-44bc-82e6-e66c8a42649d · outbound

This paper cites Physics-informed pointnet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Physics-informed pointnet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries.J

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.626009Z

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 3c1807a2-fc87-4911-b759-27648b0a4c83 · outbound

This paper cites Nonparametric boundary geometry in physics informed deep learning.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Nonparametric boundary geometry in physics informed deep learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.614509Z

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 019b2de5-6131-4a6c-8e21-79d6cf8a21c6 · outbound

This paper cites Novel deeponet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads.Comput.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Novel deeponet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads.Comput

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.603650Z

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 ff3cefcc-8955-4879-8f3a-9f86a718aab5 · outbound

This paper cites Factorized fourier neural operators.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Factorized fourier neural operators

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.592446Z

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 bf5776ad-6593-4349-b516-84c76541026f · outbound

This paper cites Unisolver: Pde-conditional transformers towards universal neural pde solvers.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Unisolver: Pde-conditional transformers towards universal neural pde solvers

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.582231Z

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 3c126ea6-4910-4aa0-86ef-f255b049440a · outbound

This paper cites Operator learning with neural fields: tackling pdes on general geometries.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Operator learning with neural fields: tackling pdes on general geometries

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.571038Z

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 27b0d1cf-c789-4852-9cbb-079ac2ab04dd · outbound

This paper cites Engsig-Karup, George Em Karniadakis, and Cheol-Ho Jeong.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Engsig-Karup, George Em Karniadakis, and Cheol-Ho Jeong

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.559449Z

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 c367d95e-4ff2-4ca0-81cd-b2ddb3c8d875 · outbound

This paper cites Point cloud neural operator for parametric pdes on complex and variable geometries.Comput.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Point cloud neural operator for parametric pdes on complex and variable geometries.Comput

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.548396Z

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 3fbe864a-ee11-469c-8481-029349eec9a6 · outbound

This paper cites Geometry-informed neural operator for large-scale 3d pdes.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Geometry-informed neural operator for large-scale 3d pdes

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.536624Z

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 49ef9242-1be0-4539-95ef-08661ca5d152 · outbound

This paper cites Gnot:ageneral neuraloperatortransformerforoperatorlearning.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Gnot:ageneral neuraloperatortransformerforoperatorlearning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.525879Z

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 e8ac50f0-79fb-4360-ab54-94c8953bfed2 · outbound

This paper cites Domain agnostic fourier neural operators.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Domain agnostic fourier neural operators

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.514867Z

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 47c21ca4-d313-44c5-bfb9-d9427038ae8d · outbound

This paper cites Transolver++: An accurate neural solver for pdes on million-scale geometries.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Transolver++: An accurate neural solver for pdes on million-scale geometries

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.503090Z

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 574f9427-a56d-4dc3-9293-e286dcfde6d0 · outbound

This paper cites Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems.Comput.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems.Comput

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.490982Z

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 948554ca-e1e3-4262-b2ea-39ca63cc3df9 · outbound

This paper cites Afictitiousdomainapproachtothedirectnumericalsimulationofincompressible viscous flow past moving rigid bodies: Application to particulate flow.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Afictitiousdomainapproachtothedirectnumericalsimulationofincompressible viscous flow past moving rigid bodies: Application to particulate flow.J

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.477813Z

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 a9938a2c-5513-4572-bc9b-2d1cbd70ee97 · outbound

This paper cites On schwarz alternating methods for nonlinear elliptic pdes.SIAM J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios On schwarz alternating methods for nonlinear elliptic pdes.SIAM J

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.465579Z

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 1756529c-54a8-4d8b-90de-64b9c10a229a · outbound

This paper cites Mionet: Learning multiple-input operators via tensor product.SIAM J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Mionet: Learning multiple-input operators via tensor product.SIAM J

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.454217Z

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 a474320f-456b-46a6-be8e-f6e3eb3b6179 · outbound

This paper cites Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:47.271650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12e1c89b-9ea6-4a99-8282-24411cc66ac8 · outbound

This paper cites On accelerated convergence of nonoverlapping schwarz methods.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios On accelerated convergence of nonoverlapping schwarz methods.J

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.441407Z

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 a682f205-0fba-4fa2-b8d3-47264ded4452 · outbound

This paper cites An analysis for a nonoverlapping domain decomposition iterative procedure.SIAM J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios An analysis for a nonoverlapping domain decomposition iterative procedure.SIAM J

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.429187Z

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 4d5341e1-dd4a-4438-9b22-a0fcc53d794b · outbound

This paper cites DeepXDE: A deep learning library for solving differential equations.SIAM Rev., 63(1):208–228, 2021.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios DeepXDE: A deep learning library for solving differential equations.SIAM Rev., 63(1):208–228, 2021

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T10:20:47.283965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c8d04df-81ee-434f-a9f2-a51387dedf5e · outbound

This paper cites Advanced Field-Solver Techniques for RC Extraction of Integrated Circuits.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Advanced Field-Solver Techniques for RC Extraction of Integrated Circuits

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.409822Z

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 45bd2a04-a2e2-4194-af2a-4fea79b161fa · outbound

This paper cites Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.397745Z

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 345a8894-6528-4e7f-ad26-5b0b00591945 · outbound

This paper cites Figueiredo, Julian L.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Figueiredo, Julian L

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.385283Z

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 9bb3858b-febd-4d99-bd29-47d36c27e8bb · outbound

This paper cites A class of finite element methods with averaging techniques for solving the three-dimensional drift-diffusion model in semiconductor device simulations.J.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios A class of finite element methods with averaging techniques for solving the three-dimensional drift-diffusion model in semiconductor device simulations.J

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.372816Z

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 94600e3f-0632-4754-9a85-6ed9a00ede0a · outbound

This paper cites Deepoheat: Operator learning-based ultra-fast thermal simulation in 3d-ic design.

DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios Deepoheat: Operator learning-based ultra-fast thermal simulation in 3d-ic design

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:20:47.359667Z

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.

Pith citing papers

Observation 57f4d412-a5de-44dc-8819-4c416bc15b27 · inbound

A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications cites this paper.

A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications DD-DeepONet: Domain decomposition and DeepONet for solving partial differential equations in three application scenarios

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:45:48.820216Z

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.