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Paper Citation Record · LEDGER

Learning cardiac activation and repolarization times with operator learning

As of 23 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.08631.

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pith.paper-citation-record.v1
2505.08631 v1

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measured 44 of 44 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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44 of 44 outbound references displayed

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Outbound references

Observation 4f0d335a-2edd-4ce6-bbb0-5889264076ea · outbound

This paper cites lifex: A flexible, high performance library for the numerical solution of complex finite element problems.SoftwareX20 (2022): 101252.

Learning cardiac activation and repolarization times with operator learning lifex: A flexible, high performance library for the numerical solution of complex finite element problems.SoftwareX20 (2022): 101252

Reference 1

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Observation 3b177977-98a1-45b3-97ee-851a87563b8e · outbound

This paper cites Al-Khatib, Dan M.

Learning cardiac activation and repolarization times with operator learning Al-Khatib, Dan M

Reference 2

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Observation ca068823-bc6e-4ee4-9a71-4cc4926f5174 · outbound

This paper cites PETSc users manual.

Learning cardiac activation and repolarization times with operator learning PETSc users manual

Reference 3

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This paper cites Kernel methods are com- petitive for operator learning.Journal of Computational Physics496 (2024): 112549.

Learning cardiac activation and repolarization times with operator learning Kernel methods are com- petitive for operator learning.Journal of Computational Physics496 (2024): 112549

Reference 4

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This paper cites The lifex library version 2.0.

Learning cardiac activation and repolarization times with operator learning The lifex library version 2.0

Reference 5

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This paper cites A comparison of Algebraic Multigrid Bidomain solvers on hybrid CPU–GPU architectures.Computer Methods in Applied Mechanics and Engineering 423 (2024): 116875.

Learning cardiac activation and repolarization times with operator learning A comparison of Algebraic Multigrid Bidomain solvers on hybrid CPU–GPU architectures.Computer Methods in Applied Mechanics and Engineering 423 (2024): 116875

Reference 6

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Observation 182c7abe-ac41-4200-ad78-a4874483aa78 · outbound

This paper cites Pavarino.

Learning cardiac activation and repolarization times with operator learning Pavarino

Reference 7

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Observation a44a4cec-9bdc-4ef2-bc44-41cd241373a5 · outbound

This paper cites H., Olivier Bernus, E.

Learning cardiac activation and repolarization times with operator learning H., Olivier Bernus, E

Reference 8

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Observation 0cad749c-d4a7-4106-813f-240d7c204a15 · outbound

This paper cites Pavarino, and Simone Scacchi.

Learning cardiac activation and repolarization times with operator learning Pavarino, and Simone Scacchi

Reference 9

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This paper cites Wavefront propagation in an activation model of the anisotropic cardiac tissue: asymptotic analysis and numerical simulations.Journal of mathematical biology28 (1990): 121-176.

Learning cardiac activation and repolarization times with operator learning Wavefront propagation in an activation model of the anisotropic cardiac tissue: asymptotic analysis and numerical simulations.Journal of mathematical biology28 (1990): 121-176

Reference 10

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This paper cites an unresolved cited work.

Learning cardiac activation and repolarization times with operator learning Unresolved cited work

Reference 11

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Observation 777786e9-3093-4d5c-bca6-8a4b9f6ec407 · outbound

This paper cites Delving deep into rectifiers: Surpass- ing human-level performance on imagenet classification.

Learning cardiac activation and repolarization times with operator learning Delving deep into rectifiers: Surpass- ing human-level performance on imagenet classification

Reference 12

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Observation 3faaa729-60f4-469c-b921-230fe78fb91a · outbound

This paper cites Impulses and physiological states in theoretical models of nerve membrane.

Learning cardiac activation and repolarization times with operator learning Impulses and physiological states in theoretical models of nerve membrane

Reference 13

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Observation e0d7dce5-ba6b-40f4-a0c7-4aa3ec64a7ff · outbound

This paper cites Deep learning-based reduced order models in cardiac electrophysiology.PloS one15, no.

Learning cardiac activation and repolarization times with operator learning Deep learning-based reduced order models in cardiac electrophysiology.PloS one15, no

Reference 14

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Observation cbaeb302-e952-4c21-9e47-ce0a5b47f7d0 · outbound

This paper cites A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials.Computer Methods in Applied Mechanics and Engineering391 (2022): 114587.

Learning cardiac activation and repolarization times with operator learning A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials.Computer Methods in Applied Mechanics and Engineering391 (2022): 114587

Reference 15

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Observation b4b439d9-1260-4b81-bb20-df17e4aa4f0e · outbound

This paper cites Fitzhugh-nagumo model.Scholarpedia1, no.

Learning cardiac activation and repolarization times with operator learning Fitzhugh-nagumo model.Scholarpedia1, no

Reference 16

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Observation 416cda72-de6d-457d-b053-521c7905a30c · outbound

This paper cites Neural tangent kernel: Convergence and generalization in neural networks.Advances in neural information processing systems31 (2018).

Learning cardiac activation and repolarization times with operator learning Neural tangent kernel: Convergence and generalization in neural networks.Advances in neural information processing systems31 (2018)

Reference 17

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Observation 044d4da9-40f4-4beb-ad75-ba103ee6dd50 · outbound

This paper cites P., and James Sneyd.Mathematical physiology1: Cellular physiology.

Learning cardiac activation and repolarization times with operator learning P., and James Sneyd.Mathematical physiology1: Cellular physiology

Reference 18

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Observation 245bc4dd-995f-4ff5-a54d-5737d8ac9110 · outbound

This paper cites An eikonal-curvature equation for action potential propagation in myocardium.

Learning cardiac activation and repolarization times with operator learning An eikonal-curvature equation for action potential propagation in myocardium

Reference 19

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Observation 15296324-4f1c-4a91-a3db-b0b44e286cd3 · outbound

This paper cites Witschey, John A.

Learning cardiac activation and repolarization times with operator learning Witschey, John A

Reference 20

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Observation b940bcc9-c893-4944-a5ec-f47048bc3334 · outbound

This paper cites On universal approximation and error bounds for Fourier neural operators.The Journal of Machine Learning Research22, no.

Learning cardiac activation and repolarization times with operator learning On universal approximation and error bounds for Fourier neural operators.The Journal of Machine Learning Research22, no

Reference 21

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Observation 21ed8985-75b8-46aa-a2dc-e6e029458b2c · outbound

This paper cites Neural operator: Learning maps between function spaces with applications to PDEs.Journal of Machine Learning Research24, no.

Learning cardiac activation and repolarization times with operator learning Neural operator: Learning maps between function spaces with applications to PDEs.Journal of Machine Learning Research24, no

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Observation d7bafa5d-6f8c-448f-a2f0-7cef31f733e8 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Learning cardiac activation and repolarization times with operator learning Fourier Neural Operator for Parametric Partial Differential Equations

Reference 23

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Observation 007977fc-1585-4b37-9bbb-3166d46a3a69 · outbound

This paper cites Michelis, Emmanuel de Bezenac, Sirani M.

Learning cardiac activation and repolarization times with operator learning Michelis, Emmanuel de Bezenac, Sirani M

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ff5a7707-f0fc-4428-ad3e-d05e453fc769 · outbound

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

Learning cardiac activation and repolarization times with operator learning Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Reference 25

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Observation 9028390e-56ca-4025-8b77-eda3ac0aa5c3 · outbound

This paper cites A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data.Computer Methods in Applied Mechanics and Engineering 393 (2022): 114778.

Learning cardiac activation and repolarization times with operator learning A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data.Computer Methods in Applied Mechanics and Engineering 393 (2022): 114778

Reference 26

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Observation fc1137e4-47d7-4eb8-842f-77ea358df713 · outbound

This paper cites Kernel methods through the roof: handling billions of points efficiently.Advances in Neural Information Pro- cessing Systems33 (2020): 14410-14422.

Learning cardiac activation and repolarization times with operator learning Kernel methods through the roof: handling billions of points efficiently.Advances in Neural Information Pro- cessing Systems33 (2020): 14410-14422

Reference 27

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Source-reported events for the cited work

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Observation 696cc165-908a-4c38-99d8-a9b194a82378 · outbound

This paper cites Kernel flows: From learning kernels from data into the abyss.Journal of Computational Physics389 (2019): 22-47.

Learning cardiac activation and repolarization times with operator learning Kernel flows: From learning kernels from data into the abyss.Journal of Computational Physics389 (2019): 22-47

Reference 28

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Observation 48623f21-773d-4251-bf62-79d137c5d2e5 · outbound

This paper cites an unresolved cited work.

Learning cardiac activation and repolarization times with operator learning Unresolved cited work

Reference 29

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Source-reported events for the cited work

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Observation 6f697ad4-d450-46a1-82c5-cda904104d42 · outbound

This paper cites Learn- ing the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks.Na- ture Communications15, no.

Learning cardiac activation and repolarization times with operator learning Learn- ing the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks.Na- ture Communications15, no

Reference 30

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Source-reported events for the cited work

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Observation 9a76c951-8eb8-443b-90b3-52b731fe75d7 · outbound

This paper cites McCulloch.

Learning cardiac activation and repolarization times with operator learning McCulloch

Reference 31

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 216cb28c-5bc1-45d2-a0be-26139a40e061 · outbound

This paper cites Cajas, Eva Casoni, and Mariano V´ azquez.

Learning cardiac activation and repolarization times with operator learning Cajas, Eva Casoni, and Mariano V´ azquez

Reference 32

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verified fuzzy
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Source-reported events for the cited work

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Observation 8550ec09-d929-439c-8e88-fa2992216804 · outbound

This paper cites Iterative kernel regression with preconditioning.Analysis and Appli- cations22, no.

Learning cardiac activation and repolarization times with operator learning Iterative kernel regression with preconditioning.Analysis and Appli- cations22, no

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T21:54:28.790948Z digest=sha256:3bb6611b251e5f91bd30d4af75cdc4ca8a01f8afe7d124cced1211af21786db0

Observation b8c4502b-e736-4650-a80c-d9046de9cb90 · outbound

This paper cites The connection between regular- ization operators and support vector kernels.Neural networks11.4 (1998): 637-649.

Learning cardiac activation and repolarization times with operator learning The connection between regular- ization operators and support vector kernels.Neural networks11.4 (1998): 637-649

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Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:29.075321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.794446Z digest=sha256:51542adb1857bfadc8c0f519ed96839321f306e966b0a8898f58fd4a6de6e5d3

Observation 6fb1eaff-b4d6-413c-8291-4e890647fe51 · outbound

This paper cites Computing the electrical activity in the heart.Springer Science & Business Media.

Learning cardiac activation and repolarization times with operator learning Computing the electrical activity in the heart.Springer Science & Business Media

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:29.063201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.798208Z digest=sha256:73d4ce58956a09e05d962f2d1cfe40597ee0b09923d4063ebe7cc12797de8903

Observation bc710fa3-45c2-45d1-9c67-07a1f45a115b · outbound

This paper cites Panfilov.

Learning cardiac activation and repolarization times with operator learning Panfilov

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:29.051045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.801900Z digest=sha256:f817c588554b113b21356dc094566095a35e7c24f55aa15254cea9bca55065f0

Observation caaf25aa-463e-4929-b3e2-35aa205f8d1d · outbound

This paper cites PDE-aware deep learning for inverse problems in cardiac electrophysiology.SIAM Journal on Scientific Computing 44, no.

Learning cardiac activation and repolarization times with operator learning PDE-aware deep learning for inverse problems in cardiac electrophysiology.SIAM Journal on Scientific Computing 44, no

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:29.038676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.806072Z digest=sha256:732f97954862e649462a4065d307793f5ed83c04fa3b8067c206d38fc23604a1

Observation 5e2d45b5-d5a7-4d28-a8e9-7c97f95ddb31 · outbound

This paper cites Popescu, and Julie K.

Learning cardiac activation and repolarization times with operator learning Popescu, and Julie K

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:29.026318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.809680Z digest=sha256:a99a8af53c4d45af9c6f6278b5f08e5f42d4d54430ae9736e4243835fe739bbb

Observation 4c64f546-9666-406e-b725-bd2d9aef2dcb · outbound

This paper cites A., Lyon, A., Shade, J., and Heijman, J.

Learning cardiac activation and repolarization times with operator learning A., Lyon, A., Shade, J., and Heijman, J

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:29.014407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.813483Z digest=sha256:0173cbd6d14a33c84fd3a612aa24c2402c5925489690eab2af7c08db84967442

Observation 1290561c-e203-47a5-9a92-0cd8fc2c4d3d · outbound

This paper cites an unresolved cited work.

Learning cardiac activation and repolarization times with operator learning Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-15T21:54:29.002034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.817249Z digest=sha256:5a66236e8e90d4eea6a3d1465bc7d991b7e07e4f829058a9a485d7cf88f5b619

Observation b43fa1d2-03e4-4fd5-9e62-e1e97809bafd · outbound

This paper cites Cardiac ventricular repolarization reserve: a principle for understanding drug-related proarrhythmic risk.British journal of pharmacology164, no.

Learning cardiac activation and repolarization times with operator learning Cardiac ventricular repolarization reserve: a principle for understanding drug-related proarrhythmic risk.British journal of pharmacology164, no

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:28.990110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.820927Z digest=sha256:ee82480ae894828876d6f83847e8280b3852cf9c92240d85b7a9ae38435cac1c

Observation 5c04cff7-5bad-4b33-ba16-74d5a5b00aa1 · outbound

This paper cites Frangi, and Alfio Quarteroni.

Learning cardiac activation and repolarization times with operator learning Frangi, and Alfio Quarteroni

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:28.977760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.824534Z digest=sha256:2e482b1801dbb2bb20ba9860db4f65b34de81c14d78080ad0b1f4a3d622e280e

Observation f5b8aea3-78f1-4f97-9453-c13f377181cc · outbound

This paper cites an unresolved cited work.

Learning cardiac activation and repolarization times with operator learning Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-15T21:54:28.964861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.828257Z digest=sha256:36dd5b8d9c1d8bccb0a7c78b3893e03151f7a32d576e62a6835d676287520e43

Observation 9fa16acf-567a-4892-bcb9-911b04bacfce · outbound

This paper cites Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control.Numerical Mathematics: Theory, Methods and Applications(2025): 104208.

Learning cardiac activation and repolarization times with operator learning Learning epidemic trajectories through Kernel Operator Learning: from modelling to optimal control.Numerical Mathematics: Theory, Methods and Applications(2025): 104208

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:54:28.951922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:54:28.831915Z digest=sha256:9c409cf32c6d618455a03c2443fbeb23f2a05cf045d8d6d8a18ff3f3ad57c59e

Pith citing papers

No inbound Pith citation observations are available.