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Paper Citation Record · LEDGER

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions

As of 10 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2507.06133.

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pith.paper-citation-record.v1
2507.06133 v2

Coverage vector

measured 51 of 51 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-08-06T17:25:44.917779Z

Reference resolution

51 of 51 outbound references displayed

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Outbound references

Observation a1d65fc7-ac34-4bf4-96b5-6766baf6e247 · outbound

This paper cites Neural operator: Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Neural operator: Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023

Reference 1

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Observation c01f5059-b0f0-4d2d-b782-73e9bc369bcc · outbound

This paper cites Brunton, Bernd R.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Brunton, Bernd R

Reference 2

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Observation a7f6656c-19dc-46cb-ab8b-604215cb57bb · outbound

This paper cites A physics-informed deep neural network for surrogate modeling in classical elasto- plasticity.Computers and Geotechnics, 2023.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions A physics-informed deep neural network for surrogate modeling in classical elasto- plasticity.Computers and Geotechnics, 2023

Reference 3

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Observation 562f3ac3-51f9-48f5-ae8a-91b01d86a203 · outbound

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

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Fourier neural operator for parametric partial differential equations

Reference 4

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Observation 5f840dbd-b74d-47b0-84e5-1a77db5e6c9a · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021

Reference 5

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Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Unresolved cited work

Reference 6

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Observation 071aa0db-04fd-402a-b4c9-09805d2c05c7 · outbound

This paper cites Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads.Engineering Applications of Artificial Intelligence, 127:107258, 2024.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads.Engineering Applications of Artificial Intelligence, 127:107258, 2024

Reference 7

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Observation e680b395-4d27-4e90-8d5e-b6c9ac526612 · outbound

This paper cites Predictions of transient vector solution fields with sequential deep operator network.Acta Mechanica, 235(8):5257–5272, 2024.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Predictions of transient vector solution fields with sequential deep operator network.Acta Mechanica, 235(8):5257–5272, 2024

Reference 8

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Observation 9e40aaf0-9271-4b40-9f6b-52df825154da · outbound

This paper cites Advanced deep operator networks to predict multiphysics solution fields in materials processing and additive manufacturing.Additive Manufacturing, 88:104266, 2024.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Advanced deep operator networks to predict multiphysics solution fields in materials processing and additive manufacturing.Additive Manufacturing, 88:104266, 2024

Reference 9

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Observation 1e5a4495-d7eb-40f7-8bba-bfff21f72659 · outbound

This paper cites Learning structured output representation using deep conditional generative models.Advances in neural information processing systems, 28, 2015.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Learning structured output representation using deep conditional generative models.Advances in neural information processing systems, 28, 2015

Reference 10

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Observation 0fb9470b-4f21-494e-99ed-60835d85842d · outbound

This paper cites Auto-encoding variational bayes, 2013.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Auto-encoding variational bayes, 2013

Reference 11

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Observation 5a124c67-9ff5-4578-8ee0-c2aa39624676 · outbound

This paper cites Generative adversarial nets.Advances in neural information processing systems, 27, 2014.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Generative adversarial nets.Advances in neural information processing systems, 27, 2014

Reference 12

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Observation f9416313-b827-424a-b767-073d0477c341 · outbound

This paper cites Neural discrete representation learning.Advances in neural information processing systems, 30, 2017.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Neural discrete representation learning.Advances in neural information processing systems, 30, 2017

Reference 13

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Observation 68eb5318-d080-44fb-a229-13d17143a75b · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermody- namics.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Deep unsupervised learning using nonequilibrium thermody- namics

Reference 14

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Observation f3a48acd-0b5c-4d93-9908-aad5dfcf3493 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33: 6840–6851, 2020.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Denoising diffusion probabilistic models.Advances in neural information processing systems, 33: 6840–6851, 2020

Reference 15

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This paper cites Cascaded diffusion models for high fidelity image generation.Journal of Machine Learning Research, 23(47):1–33, 2022.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Cascaded diffusion models for high fidelity image generation.Journal of Machine Learning Research, 23(47):1–33, 2022

Reference 16

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Observation 68aa3629-1f3e-406a-9399-2a21f8910f5c · outbound

This paper cites Classifier-Free Diffusion Guidance.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Classifier-Free Diffusion Guidance

Reference 17

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This paper cites Diffusion models beat gans on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Diffusion models beat gans on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021

Reference 18

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This paper cites Score-based generative modeling through stochastic differential equations.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Score-based generative modeling through stochastic differential equations

Reference 19

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Observation 00a7ff4f-d4d9-4edc-b33a-ff01dbeed3ce · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

Reference 20

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Observation 57be9c47-b3ce-404d-b9a4-feb643d199ff · outbound

This paper cites Mcvd-masked conditional video diffusion for prediction, generation, and interpo- lation.Advances in neural information processing systems, 35:23371–23385, 2022.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Mcvd-masked conditional video diffusion for prediction, generation, and interpo- lation.Advances in neural information processing systems, 35:23371–23385, 2022

Reference 21

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Observation 6df4d9a1-4548-4ee2-9a8f-ebca92675ee7 · outbound

This paper cites Video diffusion models.Advances in Neural Information Processing Systems, 35:8633–8646, 2022.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Video diffusion models.Advances in Neural Information Processing Systems, 35:8633–8646, 2022

Reference 22

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Observation 4e734bbb-8e84-4c09-8b24-530cef97889a · outbound

This paper cites A survey on video diffusion models.ACM Computing Surveys, 57(2):1–42, 2024.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions A survey on video diffusion models.ACM Computing Surveys, 57(2):1–42, 2024

Reference 23

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Observation 49a7870b-8d3a-4617-8bbe-276a8846117e · outbound

This paper cites Kingma, Ben Poole, Mohammad Norouzi, David J.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Kingma, Ben Poole, Mohammad Norouzi, David J

Reference 24

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Observation 1f0e45c6-e23e-4f29-8fba-32ebf44c7c82 · outbound

This paper cites Diffusion schrödinger bridge with applications to score-based generative modeling.Advances in Neural Information Processing Systems, 34:17695–17709, 2021.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Diffusion schrödinger bridge with applications to score-based generative modeling.Advances in Neural Information Processing Systems, 34:17695–17709, 2021

Reference 25

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Observation 4304484e-531b-47ee-963c-deef521223dd · outbound

This paper cites Hamprecht, Yoshua Bengio, and Aaron C.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Hamprecht, Yoshua Bengio, and Aaron C

Reference 26

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Observation 4de347a1-bc16-4578-a522-c968a94ac166 · outbound

This paper cites Generative downscaling of pde solvers with physics-guided diffusion models.Journal of Scientific Computing, 101(3):1–23, 2024.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Generative downscaling of pde solvers with physics-guided diffusion models.Journal of Scientific Computing, 101(3):1–23, 2024

Reference 27

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Observation 7f2d577e-6acd-4d5f-9b04-8aa88ed2423b · outbound

This paper cites Residual corrective diffusion modeling for km-scale atmospheric downscaling.Communications Earth & Environment, 6(1):124, 2025.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Residual corrective diffusion modeling for km-scale atmospheric downscaling.Communications Earth & Environment, 6(1):124, 2025

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Observation 04c32e51-a63c-4ea2-a972-4e250c9136e8 · outbound

This paper cites Latentpinns: Generative physics-informed neural networks via a latent representation learning.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Latentpinns: Generative physics-informed neural networks via a latent representation learning

Reference 29

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Observation 26eec72b-e8ab-4837-b0a6-982e4f2935b1 · outbound

This paper cites Generative adversarial neural operators.Transactions on Machine Learning Research, 2022.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Generative adversarial neural operators.Transactions on Machine Learning Research, 2022

Reference 30

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Observation 9ef1f93a-05c0-48f6-b75c-4debc2de6541 · outbound

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Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Unresolved cited work

Reference 31

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Observation 14f63233-fa66-47ff-83be-0672d7943ddd · outbound

This paper cites Physics-Informed Diffusion Models.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Physics-Informed Diffusion Models

Reference 32

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Observation 0da82717-4d9f-4160-ac2a-7a8c4f6d8e98 · outbound

This paper cites Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling

Reference 33

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Observation d1dbebaf-6005-4fa3-948a-66eea7f8391f · outbound

This paper cites Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO).

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)

Reference 34

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 8e804e2d-d963-4f95-805f-5d65070bf6c7 · outbound

This paper cites Reynolds-averaged navier–stokes equations for turbulence modeling.Applied Mechanics Reviews, 62(4):040802, 06 2009.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Reynolds-averaged navier–stokes equations for turbulence modeling.Applied Mechanics Reviews, 62(4):040802, 06 2009

Reference 35

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Observation 1521cc25-75f4-4332-9597-7634c8f62a08 · outbound

This paper cites Abaqus/standard user’s manual version 2024, 2024.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Abaqus/standard user’s manual version 2024, 2024

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 70bda187-fefe-4999-b9fe-53d33150296b · outbound

This paper cites Simo and Thomas J.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Simo and Thomas J

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 4c706e6e-39b3-46c8-8727-f880fb6322b0 · outbound

This paper cites Dover Publications, Mineola, NY , 2008.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Dover Publications, Mineola, NY , 2008

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 06d9b58e-4b10-42ba-a3ca-188c97037e73 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 39

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Observation fdaad7d4-e793-4828-ad72-1c5530fa3a0e · outbound

This paper cites Edict: Exact diffusion inversion via coupled transformations.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Edict: Exact diffusion inversion via coupled transformations

Reference 40

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation ee495a31-149e-49bd-9c53-e0756f42eaac · outbound

This paper cites Film: Visual reasoning with a general conditioning layer.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Film: Visual reasoning with a general conditioning layer

Reference 41

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Observation ac399260-97ce-48c6-9fb0-aa49c7bbe19d · outbound

This paper cites Focal loss for dense object detection.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Focal loss for dense object detection

Reference 42

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 0033ffca-cb0d-4a67-ad4a-41c4df645bde · outbound

This paper cites Resshift: Efficient diffusion model for image super-resolution by residual shifting.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Resshift: Efficient diffusion model for image super-resolution by residual shifting

Reference 43

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 6b6fe9a7-9c5d-434f-8f9c-53cb1bc46d42 · outbound

This paper cites Residual denoising diffusion models.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Residual denoising diffusion models

Reference 44

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation d9f508bd-4340-4f30-bbc0-7b5137a3fe9e · outbound

This paper cites an unresolved cited work.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Unresolved cited work

Reference 45

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation aa772cc0-d8ed-4d6c-a2ce-93bc2dc0873f · outbound

This paper cites Denoising diffusion error correction codes.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Denoising diffusion error correction codes

Reference 46

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verified fuzzy
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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation a6d23fd5-e1af-4c2d-855b-11c5bd4df42a · outbound

This paper cites Ansys fluent user’s guide, release 2024 r1, 2024.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Ansys fluent user’s guide, release 2024 r1, 2024

Reference 47

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 9ec90abf-8bc3-4072-a042-623403e660c4 · outbound

This paper cites Progressive distillation for fast sampling of diffusion models.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Progressive distillation for fast sampling of diffusion models

Reference 48

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Source-reported events for the cited work

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Observation f90a954b-dd16-4e63-84d0-2d49968c4b14 · outbound

This paper cites Consistency models.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Consistency models

Reference 49

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no resolver link, observed 2026-08-06T19:16:30.914719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:16:30.914719Z digest=sha256:abc0f06a342c2e473559ef6efa5338c2e44925ee5c862a10edaac38bf8fef4e1

Observation 739aec81-33a8-47d1-a0aa-1181fb3fab40 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 50

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no resolver link, observed 2026-08-06T19:16:30.917532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:16:30.917532Z digest=sha256:60803f2838a3de7ad0b70c8fd4e4d6e7abe41ad061ce8636202e46d7fc043045

Observation eaaaf431-9dda-47df-ba2c-da7d3f502b0e · outbound

This paper cites Gnot: A general neural operator transformer for operator learning.

Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions Gnot: A general neural operator transformer for operator learning

Reference 51

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Pith citing papers

Observation 92db63d2-9a95-4d6c-8c56-27ec458afee0 · inbound

Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators cites this paper.

Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions

Reference 17

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verified exact
local_arxiv, observed 2026-08-06T17:25:45.068075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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