Automatic reproducing kernel and regularization for learning convolution kernels | Paper Citation Record

Observation a6b23299-16e9-4cdb-b761-aa99f28f347d · outbound

This paper cites Neural operator: Graph kernel network for partial differential equations.

Automatic reproducing kernel and regularization for learning convolution kernels Neural operator: Graph kernel network for partial differential equations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:19.177027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96fd4c61-ce54-4ae7-8a68-cee1abc0c916 · outbound

This paper cites Lévy processes and stochastic calculus.

Automatic reproducing kernel and regularization for learning convolution kernels Lévy processes and stochastic calculus

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:29.162779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 19bacbb8-97da-49bd-8d1e-74d10873177d · outbound

This paper cites Theory of reproducing kernels.Transactions of the American mathematical society, 68(3):337–404, 1950.

Automatic reproducing kernel and regularization for learning convolution kernels Theory of reproducing kernels.Transactions of the American mathematical society, 68(3):337–404, 1950

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:19.392645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a129040-e94d-42e4-a9c3-24ac805cf923 · outbound

This paper cites Application of a fractional advection-dispersion equation.

Automatic reproducing kernel and regularization for learning convolution kernels Application of a fractional advection-dispersion equation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:29.031216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6b90730b-9e43-4a5b-8f04-374a64d8dc93 · outbound

This paper cites Image denoising methods.

Automatic reproducing kernel and regularization for learning convolution kernels Image denoising methods

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:28.878427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation be869a2a-8171-4c5c-be63-0464a6e367ac · outbound

This paper cites Aggregation-diffusion equations: dynamics, asymptotics, and singular limits.

Automatic reproducing kernel and regularization for learning convolution kernels Aggregation-diffusion equations: dynamics, asymptotics, and singular limits

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:28.710802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 289e04de-f195-4886-8f1f-341e433285fb · outbound

This paper cites Convergence analysis of LSQR for compact operator equa- tions.

Automatic reproducing kernel and regularization for learning convolution kernels Convergence analysis of LSQR for compact operator equa- tions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:28.595901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bb690bb1-fd87-479b-807b-8b1fd5a0c942 · outbound

This paper cites A data-adaptive RKHS prior for Bayesian learning of kernels in operators.Journal of Machine Learning Research, 25(317):1–37, 2024.

Automatic reproducing kernel and regularization for learning convolution kernels A data-adaptive RKHS prior for Bayesian learning of kernels in operators.Journal of Machine Learning Research, 25(317):1–37, 2024

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:28.457787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3fc2df67-eef9-43b9-9abd-8ee1e2674f2d · outbound

This paper cites Solving and learning nonlinear PDEs with gaussian processes.Journal of Computational Physics, 447:110668, 2021.

Automatic reproducing kernel and regularization for learning convolution kernels Solving and learning nonlinear PDEs with gaussian processes.Journal of Computational Physics, 447:110668, 2021

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:28.346026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8bbfba20-d631-4cf9-a2d0-19b4f9bd385c · outbound

This paper cites Heat kernels for non-symmetric non-local operators.Recent developments in nonlocal theory, pages 24–51, 2018.

Automatic reproducing kernel and regularization for learning convolution kernels Heat kernels for non-symmetric non-local operators.Recent developments in nonlocal theory, pages 24–51, 2018

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:28.199267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b65c244e-70be-4488-9490-6c2599268e4a · outbound

This paper cites A weighted-GCV method for Lanczos- hybrid regularization.

Automatic reproducing kernel and regularization for learning convolution kernels A weighted-GCV method for Lanczos- hybrid regularization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:28.091449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9504819a-6b1e-4e5e-be9f-02db4bbba6d1 · outbound

This paper cites On the mathematical foundations of learning.

Automatic reproducing kernel and regularization for learning convolution kernels On the mathematical foundations of learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:27.953249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a88e8f8-a9cf-48d3-9f09-d44f3d9d4701 · outbound

This paper cites Learning theory: an approximation theory viewpoint, volume24.

Automatic reproducing kernel and regularization for learning convolution kernels Learning theory: an approximation theory viewpoint, volume24

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:27.822851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dd89202a-bc40-41a4-a431-3be4cf620989 · outbound

This paper cites One- shot learning of stochastic differential equations with data adapted kernels.Physica D: Nonlinear Phenomena, 444:133583, 2023.

Automatic reproducing kernel and regularization for learning convolution kernels One- shot learning of stochastic differential equations with data adapted kernels.Physica D: Nonlinear Phenomena, 444:133583, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:27.654625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8953a6b4-8049-4553-b28c-485790846c1a · outbound

This paper cites Learningdynamicalsystemsfromdata: asimplecross-validationperspective, partii: nonparametric kernel flows.Physica D: Nonlinear Phenomena, 476:134641, 2025.

Automatic reproducing kernel and regularization for learning convolution kernels Learningdynamicalsystemsfromdata: asimplecross-validationperspective, partii: nonparametric kernel flows.Physica D: Nonlinear Phenomena, 476:134641, 2025

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:27.531334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 00713798-ab2c-47a2-ba9e-3287ee7a4653 · outbound

This paper cites Nu- merical methods for nonlocal and fractional models.Acta Numerica, 29:1–124, 2020.

Automatic reproducing kernel and regularization for learning convolution kernels Nu- merical methods for nonlocal and fractional models.Acta Numerica, 29:1–124, 2020

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:27.357261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44c780fe-e1fb-4ddc-ab94-64cb1f2f8e21 · outbound

This paper cites Analysis and approximation of nonlocal diffusion problems with volume constraints.SIAM review, 54(4):667–696, 2012.

Automatic reproducing kernel and regularization for learning convolution kernels Analysis and approximation of nonlocal diffusion problems with volume constraints.SIAM review, 54(4):667–696, 2012

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:27.105489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2ceffe3b-b1df-453c-8ae8-40a4e29435c3 · outbound

This paper cites An introduction to stochastic dynamics, volume 51.

Automatic reproducing kernel and regularization for learning convolution kernels An introduction to stochastic dynamics, volume 51

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:26.872268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation aa89f2c2-ec08-448a-b5f5-876eef3e2cdc · outbound

This paper cites Using the l–curve for determining optimal regularization parameters.

Automatic reproducing kernel and regularization for learning convolution kernels Using the l–curve for determining optimal regularization parameters

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:26.750221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9bb6119f-54d4-474b-9b8a-50b67282de0c · outbound

This paper cites Regularization of inverse problems, volume 375.

Automatic reproducing kernel and regularization for learning convolution kernels Regularization of inverse problems, volume 375

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:26.622178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cb754077-7e9b-4ae4-bd96-2283de93f9eb · outbound

This paper cites Learning particle swarming models from data with gaussian processes.Mathematics of Computation, 93(349):2391–2437, 2024.

Automatic reproducing kernel and regularization for learning convolution kernels Learning particle swarming models from data with gaussian processes.Mathematics of Computation, 93(349):2391–2437, 2024

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:26.442313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1b9b5f7f-3d08-413f-8899-6a7b1f6d0b6f · outbound

This paper cites Nonlocaloperatorswithapplicationstoimageprocessing.

Automatic reproducing kernel and regularization for learning convolution kernels Nonlocaloperatorswithapplicationstoimageprocessing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:26.365936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation af4f1bb1-901f-404d-95c9-2d8506c14331 · outbound

This paper cites Generalized cross-validation as a method for choosing a good ridge parameter.Technometrics, 21(2):215–223, 1979.

Automatic reproducing kernel and regularization for learning convolution kernels Generalized cross-validation as a method for choosing a good ridge parameter.Technometrics, 21(2):215–223, 1979

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:26.262079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a579dc65-a552-4104-9884-2a859ab3d2bc · outbound

This paper cites A distribution-free theory of nonparametric regression.

Automatic reproducing kernel and regularization for learning convolution kernels A distribution-free theory of nonparametric regression

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:26.146475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8afae4a4-d443-4d0c-bf56-45ebdf44ef4f · outbound

This paper cites SIAM, 2010.

Automatic reproducing kernel and regularization for learning convolution kernels SIAM, 2010

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:26.053329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5c36c7a7-b354-4740-b9ae-a575a72c1dc5 · outbound

This paper cites Kernel methods for bayesian elliptic inverse problems on manifolds.SIAM/ASA Journal on Uncertainty Quantification, 8(4):1414–1445, 2020.

Automatic reproducing kernel and regularization for learning convolution kernels Kernel methods for bayesian elliptic inverse problems on manifolds.SIAM/ASA Journal on Uncertainty Quantification, 8(4):1414–1445, 2020

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.926026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 83c7a09e-624c-480b-bd59-0a94e78c807a · outbound

This paper cites A general peridynamics model for multiphase transport of non-newtonian compressible fluids in porous media.Journal of Computational Physics, 402:109075, 2020.

Automatic reproducing kernel and regularization for learning convolution kernels A general peridynamics model for multiphase transport of non-newtonian compressible fluids in porous media.Journal of Computational Physics, 402:109075, 2020

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.786456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 62d71727-c7d0-4d69-9827-fe932d476edc · outbound

This paper cites Choosing regularization parameters in iterative methods for ill-posed problems.SIAM J.

Automatic reproducing kernel and regularization for learning convolution kernels Choosing regularization parameters in iterative methods for ill-posed problems.SIAM J

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.707990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b67f988-8993-42b7-bd37-377ab7ab5d81 · 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.

Automatic reproducing kernel and regularization for learning convolution kernels Neural operator: Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.558136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3cb6eb17-c038-492e-963b-1b185f81ed55 · outbound

This paper cites Identifiability of interaction kernels in mean-field equations of interacting particles.

Automatic reproducing kernel and regularization for learning convolution kernels Identifiability of interaction kernels in mean-field equations of interacting particles

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.418263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dd3a1f3c-f7aa-431d-b384-d4d9152cc24d · outbound

This paper cites Small noise analysis for Tikhonov and RKHS regularizations.

Automatic reproducing kernel and regularization for learning convolution kernels Small noise analysis for Tikhonov and RKHS regularizations

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:22.182951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1130a908-fa41-4c6a-b835-435fbf532a84 · outbound

This paper cites A preconditioned krylov subspace method for linear inverse problems with general-form tikhonov regularization.

Automatic reproducing kernel and regularization for learning convolution kernels A preconditioned krylov subspace method for linear inverse problems with general-form tikhonov regularization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.326743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 171c3d5c-e586-405e-9d9b-7c236b6a5c48 · outbound

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

Automatic reproducing kernel and regularization for learning convolution kernels Fourier Neural Operator for Parametric Partial Differential Equations

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:22.338341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c49fd56f-01db-4bf0-88d1-25bdfaf5c03e · outbound

This paper cites Image recovery via nonlocal operators.

Automatic reproducing kernel and regularization for learning convolution kernels Image recovery via nonlocal operators

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.234609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6dd14e2d-b9aa-4634-b94e-259efaa4c6e5 · outbound

This paper cites Nonparametric learning of kernels in nonlocal operators.Journal of Peridynamics and Nonlocal Modeling, pages 1–24, 2023.

Automatic reproducing kernel and regularization for learning convolution kernels Nonparametric learning of kernels in nonlocal operators.Journal of Peridynamics and Nonlocal Modeling, pages 1–24, 2023

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.160553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cc0d5a42-0478-4012-ad5f-ea2505f8f37f · outbound

This paper cites Data adaptive RKHS Tikhonov regularization for learning kernels in operators.Proceedings of Mathematical and Scientific Machine Learning, PMLR 190:158- 172, 2022.

Automatic reproducing kernel and regularization for learning convolution kernels Data adaptive RKHS Tikhonov regularization for learning kernels in operators.Proceedings of Mathematical and Scientific Machine Learning, PMLR 190:158- 172, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:25.047429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dedaaea3-683c-4443-85a6-a3b70d61a836 · outbound

This paper cites An adaptive RKHS regularization for the Fredholm integral equations.

Automatic reproducing kernel and regularization for learning convolution kernels An adaptive RKHS regularization for the Fredholm integral equations

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:24.923913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 37cd561e-7dcf-4c52-84f7-f5fc2c176e49 · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Automatic reproducing kernel and regularization for learning convolution kernels DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:22.784595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edd4c822-68f0-40d1-ab06-177e35f8e7ff · outbound

This paper cites Kernel flows: From learning kernels from data into the abyss.

Automatic reproducing kernel and regularization for learning convolution kernels Kernel flows: From learning kernels from data into the abyss

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:24.759479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7eca4fb1-0b9a-4d96-9cbc-47c4b7997cd1 · outbound

This paper cites LSQR: An algorithm for sparse linear equations and sparse least squares.ACM Transactions on Mathematical Software (TOMS), 8(1):43–71, 1982.

Automatic reproducing kernel and regularization for learning convolution kernels LSQR: An algorithm for sparse linear equations and sparse least squares.ACM Transactions on Mathematical Software (TOMS), 8(1):43–71, 1982

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:24.668037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d2ac9059-e163-4e73-a062-37b99febfcd4 · outbound

This paper cites Peridynamic states and constitutive modeling.Journal of elasticity, 88:151–184, 2007.

Automatic reproducing kernel and regularization for learning convolution kernels Peridynamic states and constitutive modeling.Journal of elasticity, 88:151–184, 2007

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:24.520145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44d58b28-77ae-4420-a43c-3ee01f477649 · outbound

This paper cites Convergence rates of certain approximate solutions to fredholm integral equations of the first kind.Journal of Approximation Theory, 7(2):167–185, 1973.

Automatic reproducing kernel and regularization for learning convolution kernels Convergence rates of certain approximate solutions to fredholm integral equations of the first kind.Journal of Approximation Theory, 7(2):167–185, 1973

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:24.393386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d34778cf-51b0-43da-b5a1-d6fe460dec9a · outbound

This paper cites Practical approximate solutions to linear operator equations when the data are noisy.

Automatic reproducing kernel and regularization for learning convolution kernels Practical approximate solutions to linear operator equations when the data are noisy

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:23.095480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f145676-f438-4303-8561-e44cdf24acad · outbound

This paper cites SIAM, 1990.

Automatic reproducing kernel and regularization for learning convolution kernels SIAM, 1990

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:23.185440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51705728-2700-4a09-a019-b48ac7595673 · outbound

This paper cites Non-local neural networks.

Automatic reproducing kernel and regularization for learning convolution kernels Non-local neural networks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:23.231901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a775a39-4776-4746-a52e-3cecf39bd6e1 · outbound

This paper cites A data-driven peridynamic continuum model for upscaling molecular dynamics.Computer Methods in Applied Mechanics and Engineering, 389:114400, 2022.

Automatic reproducing kernel and regularization for learning convolution kernels A data-driven peridynamic continuum model for upscaling molecular dynamics.Computer Methods in Applied Mechanics and Engineering, 389:114400, 2022

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:24.257818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9368a3d7-1348-4a2a-9995-a4be9688eed3 · outbound

This paper cites Nonlocal operator learning for homog- enized models: From high-fidelity simulations to constitutive laws.Journal of Peridynamics and Nonlocal Modeling, 6(4):709–724, 2024.

Automatic reproducing kernel and regularization for learning convolution kernels Nonlocal operator learning for homog- enized models: From high-fidelity simulations to constitutive laws.Journal of Peridynamics and Nonlocal Modeling, 6(4):709–724, 2024

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:24.093418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 57df9d30-36f0-45f5-ac1a-fcff17a2092a · outbound

This paper cites A reproducing kernel Hilbert space approach to functional linear regression.

Automatic reproducing kernel and regularization for learning convolution kernels A reproducing kernel Hilbert space approach to functional linear regression

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:23.924000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ebc099b4-e93e-4b94-86f3-ba96c35286d6 · outbound

This paper cites Estimating linear response statistics using orthogonal polynomials: An rkhs formulation.Foundations of Data Science, 2(4):443–485, 2020.

Automatic reproducing kernel and regularization for learning convolution kernels Estimating linear response statistics using orthogonal polynomials: An rkhs formulation.Foundations of Data Science, 2(4):443–485, 2020

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:12:23.789880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 07bd3e1f-9911-481c-9123-9b767c34739a · outbound

This paper cites Minimax rates for learning kernels in operators.

Automatic reproducing kernel and regularization for learning convolution kernels Minimax rates for learning kernels in operators

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:23.632495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers