ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance | Paper Citation Record

Observation 513557b2-27e9-43d5-93ad-0dc9e30e54c0 · outbound

This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Neural Operator: Graph Kernel Network for Partial Differential Equations

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This paper cites Representation Equivalent Neural Operators: A Framework for Alias-free Operator Learning.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Representation Equivalent Neural Operators: A Framework for Alias-free Operator Learning

Reference 2

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This paper cites A survey of projection-based model reduction methods for parametric dynamical systems.SIAM Review, 57(4):483–531, jan 2015.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance A survey of projection-based model reduction methods for parametric dynamical systems.SIAM Review, 57(4):483–531, jan 2015

Reference 3

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ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance finite-element

Reference 4

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Observation 4de637a2-bbb1-4d8e-8a95-fd143d7efb07 · outbound

This paper cites Kovachki, and Andrew M.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Kovachki, and Andrew M

Reference 5

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This paper cites Convergence rates for greedy algorithms in reduced basis methods.SIAM Journal on Mathematical Analysis, 43(3):1457–1472, 2011.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Convergence rates for greedy algorithms in reduced basis methods.SIAM Journal on Mathematical Analysis, 43(3):1457–1472, 2011

Reference 6

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This paper cites Convergence and error control of consistent PINNs for elliptic PDEs.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Convergence and error control of consistent PINNs for elliptic PDEs

Reference 7

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This paper cites Springer, 2008.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Springer, 2008

Reference 8

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This paper cites Model reduction on manifolds: A differential geometric framework.Physica D: Nonlinear Phenomena, 468:134299, 2024.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Model reduction on manifolds: A differential geometric framework.Physica D: Nonlinear Phenomena, 468:134299, 2024

Reference 9

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This paper cites Physics-informed neural networks (PINNs) for fluid mechanics: A review.Acta Mechanica Sinica, pages 1–12, 2022.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Physics-informed neural networks (PINNs) for fluid mechanics: A review.Acta Mechanica Sinica, pages 1–12, 2022

Reference 10

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This paper cites Choose a transformer: Fourier or galerkin.Advances in neural information processing systems, 34:24924–24940, 2021.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Choose a transformer: Fourier or galerkin.Advances in neural information processing systems, 34:24924–24940, 2021

Reference 11

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This paper cites Machine learning and the physical sciences.Reviews of Modern Physics, 91(4):045002, 2019.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Machine learning and the physical sciences.Reviews of Modern Physics, 91(4):045002, 2019

Reference 12

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Observation 29430890-a516-4c80-895b-1d4bd5098141 · outbound

This paper cites Chen and H.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Chen and H

Reference 13

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Observation 56a4b852-bbbd-48dc-b976-3c011082818d · outbound

This paper cites TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs.Computer Methods in Applied Mechanics and Engineering, 430:117198, 2024.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs.Computer Methods in Applied Mechanics and Engineering, 430:117198, 2024

Reference 14

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This paper cites GPT-PINN: Generative pre-trained physics-informed neural networks toward non-intrusive meta-learning of parametric pdes.Finite Elements in Analysis and Design, 228:104047, 2024.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance GPT-PINN: Generative pre-trained physics-informed neural networks toward non-intrusive meta-learning of parametric pdes.Finite Elements in Analysis and Design, 228:104047, 2024

Reference 15

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This paper cites de Hoop, Daniel Zhengyu Huang, Elizabeth Qian, and Andrew M.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance de Hoop, Daniel Zhengyu Huang, Elizabeth Qian, and Andrew M

Reference 16

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This paper cites Approximation rates of deeponets for learning operators arising from advection–diffusion equations.Neural Networks, 153:411–426, 2022.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Approximation rates of deeponets for learning operators arising from advection–diffusion equations.Neural Networks, 153:411–426, 2022

Reference 17

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This paper cites One-Shot Transfer Learning of Physics-Informed Neural Networks.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance One-Shot Transfer Learning of Physics-Informed Neural Networks

Reference 18

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This paper cites Machine-learning-assisted modeling.Physics Today, 74(7):36–41, 2021.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Machine-learning-assisted modeling.Physics Today, 74(7):36–41, 2021

Reference 19

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This paper cites Limitations of physics informed machine learning for nonlinear two-phase transport in porous media.Journal of Machine Learning for Modeling and Computing, 1(1), 2020.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Limitations of physics informed machine learning for nonlinear two-phase transport in porous media.Journal of Machine Learning for Modeling and Computing, 1(1), 2020

Reference 20

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This paper cites Knowledge-based modeling of material behavior with neural networks.Journal of engineering mechanics, 117(1):132–153, 1991.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Knowledge-based modeling of material behavior with neural networks.Journal of engineering mechanics, 117(1):132–153, 1991

Reference 21

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Observation 405e18e5-b1d5-4e09-86a0-25ba73b576ac · outbound

This paper cites Autoprogressive training of neural network constitutive models.International Journal for Numerical Methods in Engineering, 42(1):105–126, 1998.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Autoprogressive training of neural network constitutive models.International Journal for Numerical Methods in Engineering, 42(1):105–126, 1998

Reference 22

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This paper cites Loss landscape engineering via data regulation on pinns.Machine Learning with Applications, 12:100464, 2023.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Loss landscape engineering via data regulation on pinns.Machine Learning with Applications, 12:100464, 2023

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:31.122527Z

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This paper cites Transfer learning enhanced physics informed neural network for phase-field modeling of fracture.Theoretical and Applied Fracture Mechanics, 106:102447, 2020.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Transfer learning enhanced physics informed neural network for phase-field modeling of fracture.Theoretical and Applied Fracture Mechanics, 106:102447, 2020

Reference 24

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Observation 23b9ba83-3a96-42be-bd29-8ac35b3f9b59 · outbound

This paper cites Physics-informeddeepneural operator networks.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Physics-informeddeepneural operator networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:31.527538Z

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This paper cites Multiwavelet-based operator learning for differential equations.Advances in neural information processing systems, 34:24048–24062, 2021.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Multiwavelet-based operator learning for differential equations.Advances in neural information processing systems, 34:24048–24062, 2021

Reference 26

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ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Unresolved cited work

Reference 27

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This paper cites An equivariant neural operator for developing nonlocal tensorial constitutive models.Journal of Computational Physics, 488:112243, 2023.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance An equivariant neural operator for developing nonlocal tensorial constitutive models.Journal of Computational Physics, 488:112243, 2023

Reference 28

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This paper cites Manifoldlearningbaseddata-driven modeling for soft biological tissues.Journal of Biomechanics, 117:110124, 2021.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Manifoldlearningbaseddata-driven modeling for soft biological tissues.Journal of Biomechanics, 117:110124, 2021

Reference 29

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ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance SpringerBriefs in Mathematics

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:32.155631Z

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This paper cites Peridynamic neural operators: A data-driven nonlocal constitutive model for complex material responses.Computer Methods in Applied Mechanics and Engineering, 425:116914, 2024.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Peridynamic neural operators: A data-driven nonlocal constitutive model for complex material responses.Computer Methods in Applied Mechanics and Engineering, 425:116914, 2024

Reference 31

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Observation ae40657e-e455-482a-a3fb-39bfdb67a7ff · outbound

This paper cites Physics-informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Physics-informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021

Reference 32

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This paper cites Meta-learning loss functions of parametric partial differential equations using physics-informed neural networks.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Meta-learning loss functions of parametric partial differential equations using physics-informed neural networks

Reference 33

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This paper cites Onuniversalapproximationanderrorbounds for fourier neural operators.Journal of Machine Learning Research, 22(290):1–76, 2021.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Onuniversalapproximationanderrorbounds for fourier neural operators.Journal of Machine Learning Research, 22(290):1–76, 2021

Reference 34

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This paper cites Chapter9-Operatorlearning: Algorithms and analysis.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Chapter9-Operatorlearning: Algorithms and analysis

Reference 35

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

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Neural operator: Learning maps between function spaces with applications to PDEs.Journal of Machine Learning Research, 24(89):1–97, 2023

Reference 36

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Observation ac36d249-45af-4e9f-a048-88b4c2b0180b · outbound

This paper cites Galerkin proper orthogonal decomposition methods for a general equation in fluid dynamics.SIAM J.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Galerkin proper orthogonal decomposition methods for a general equation in fluid dynamics.SIAM J

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:33.004983Z

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This paper cites Operator learning with PCA-Net: upper and lower complexity bounds.Journal of Machine Learning Research, 24(318):1–67, 2023.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Operator learning with PCA-Net: upper and lower complexity bounds.Journal of Machine Learning Research, 24(318):1–67, 2023

Reference 38

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Observation f89d96da-5270-45f2-bdaf-1bc92f8621a1 · outbound

This paper cites Data-drivendesignformetamaterials and multiscale systems: A review.Advanced Materials, 36(8):2305254, 2024.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Data-drivendesignformetamaterials and multiscale systems: A review.Advanced Materials, 36(8):2305254, 2024

Reference 39

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Observation 02ad2051-047d-420f-9e37-b1edb8b89f94 · outbound

This paper cites SIAM, 2007.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance SIAM, 2007

Reference 40

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Observation 8c6cd052-c36b-41b8-b8e4-a0a7b56af6ce · outbound

This paper cites Multipole graph neural operator for parametric partial differential equations.Advances in Neural Information Processing Systems, 33:NeurIPS 2020, 2020.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Multipole graph neural operator for parametric partial differential equations.Advances in Neural Information Processing Systems, 33:NeurIPS 2020, 2020

Reference 41

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

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Fourier neural operator for parametric partial differential equations

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:33.770239Z

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Observation 771f0685-3797-4331-8d5b-f3b4988572bb · outbound

This paper cites Physics-Informed Neural Operator for Learning Partial Differential Equations.ACM / IMS J.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Physics-Informed Neural Operator for Learning Partial Differential Equations.ACM / IMS J

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:33.897453Z

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This paper cites Domain agnostic fourier neural operators.Advances in Neural Information Processing Systems, 36, 2024.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Domain agnostic fourier neural operators.Advances in Neural Information Processing Systems, 36, 2024

Reference 44

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This paper cites Rajanna, Edward W.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Rajanna, Edward W

Reference 45

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Observation 16b501b3-c82b-4993-b11c-a75450e92dae · 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.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators.Nature Machine Intelligence, 3(3):218–229, 2021

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:34.367014Z

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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, 393:114778, 2022.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data.Computer Methods in Applied Mechanics and Engineering, 393:114778, 2022

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:34.491665Z

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This paper cites Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:34.661488Z

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Observation 5200fb8e-3f2e-4fa1-96bb-8620014467ad · outbound

This paper cites A reduced-basis element method.Journal of scientific computing, 17:447–459, 2002.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance A reduced-basis element method.Journal of scientific computing, 17:447–459, 2002

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:34.778315Z

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This paper cites Weakbaselinesandreportingbiasesleadtooveroptimisminmachine learning for fluid-related partial differential equations.Nature Machine Intelligence, pages 1–14, 2024.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Weakbaselinesandreportingbiasesleadtooveroptimisminmachine learning for fluid-related partial differential equations.Nature Machine Intelligence, pages 1–14, 2024

Reference 50

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This paper cites Integral Autoencoder Network for Discretization- Invariant Learning.Journal of Machine Learning Research, 23:1–45, 2022.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Integral Autoencoder Network for Discretization- Invariant Learning.Journal of Machine Learning Research, 23:1–45, 2022

Reference 51

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Observation 63648100-8b05-4b19-9c0c-c17c92b206c7 · outbound

This paper cites Ab initio solution of the many-electron schrödinger equation with deep neural networks.Physical Review Research, 2(3):033429, 2020.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Ab initio solution of the many-electron schrödinger equation with deep neural networks.Physical Review Research, 2(3):033429, 2020

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:35.364688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffba99dc-5a12-4a34-ab0b-6d5fde5c14e6 · outbound

This paper cites Physics- informedneuralnetworkwithtransferlearning(tl-pinn)basedondomainsimilaritymeasureforprediction of nuclear reactor transients.Scientific Reports, 13(1):16840, 2023.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Physics- informedneuralnetworkwithtransferlearning(tl-pinn)basedondomainsimilaritymeasureforprediction of nuclear reactor transients.Scientific Reports, 13(1):16840, 2023

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:35.561474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4c5d729-252c-479e-b1f2-daa237e51da8 · outbound

This paper cites Springer International Publishing, Cham, 2016.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Springer International Publishing, Cham, 2016

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:35.712976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6e4430d-fcb6-4301-b9da-b67e83f891d9 · outbound

This paper cites an unresolved cited work.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Unresolved cited work

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:35.923315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcc74d4e-5f1d-4701-a6de-400bffeb1253 · outbound

This paper cites Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.Science, 367(6481):1026–1030, 2020.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations.Science, 367(6481):1026–1030, 2020

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:36.148573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d807247-d136-47b5-8390-58d7b9cd7025 · outbound

This paper cites Convolutional Neural Operators for robust and accurate learning of PDEs.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Convolutional Neural Operators for robust and accurate learning of PDEs

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:36.282473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1c425dc-432c-426c-9047-c19e14d80841 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:36.434573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22d473af-4fc3-4040-a30a-2a2e3bb1cff3 · outbound

This paper cites Hyposvi: Hypocentre inversion with stein variational inference and physics informed neural networks.Geophysical Journal International, 228(1):698–710, 2022.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Hyposvi: Hypocentre inversion with stein variational inference and physics informed neural networks.Geophysical Journal International, 228(1):698–710, 2022

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:36.647707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7975601-cbd7-4f33-9a08-df0342369320 · outbound

This paper cites Factorized Fourier Neural Operators.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Factorized Fourier Neural Operators

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:36.771803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7043ef48-5e6c-40d3-8dc0-2c8e32af96e6 · outbound

This paper cites Understanding and mitigating gradient flow pathologies in physics-informed neural networks.SIAM Journal on Scientific Computing, 43(5):A3055–A3081, 2021.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Understanding and mitigating gradient flow pathologies in physics-informed neural networks.SIAM Journal on Scientific Computing, 43(5):A3055–A3081, 2021

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:36.962658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d83eca6-de0f-4cdb-ad3d-ed966eb2fcb8 · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed deeponets.Science advances, 7(40):eabi8605, 2021.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Learning the solution operator of parametric partial differential equations with physics-informed deeponets.Science advances, 7(40):eabi8605, 2021

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:37.051953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 189b1d07-ced9-4fab-9348-80f2f9ece0b3 · outbound

This paper cites When and why pinns fail to train: A neural tangent kernel perspective.Journal of Computational Physics, 449:110768, 2022.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance When and why pinns fail to train: A neural tangent kernel perspective.Journal of Computational Physics, 449:110768, 2022

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:37.240442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 319ccc2a-4ab3-4e03-b98a-e14116a976ac · outbound

This paper cites U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow.Advances in Water Resources, 163:104180, 2022.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow.Advances in Water Resources, 163:104180, 2022

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:37.467034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b6628af-56d0-479e-9088-97448cddd745 · outbound

This paper cites Transolver: A fast transformer solver for PDEs on general geometries.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Transolver: A fast transformer solver for PDEs on general geometries

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:37.667551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81bf83bd-e68f-44c2-9e0b-0eafe01fc1a2 · outbound

This paper cites Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:37.839882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb6247c0-5d9a-499c-9185-ffde0989d44e · outbound

This paper cites Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:38.020832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8ba24fc-c122-4999-ac2a-318c9368f94b · outbound

This paper cites Learning deep implicit fourier neural operators (ifnos) with applications to heterogeneous material modeling.Computer Methods in Applied Mechanics and Engineering, 398:115296, 2022.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Learning deep implicit fourier neural operators (ifnos) with applications to heterogeneous material modeling.Computer Methods in Applied Mechanics and Engineering, 398:115296, 2022

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:38.204652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b38c305-5128-49cd-8861-40750cb9657c · outbound

This paper cites Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics.Physical Review Letters, 120(14):143001, 2018.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics.Physical Review Letters, 120(14):143001, 2018

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:38.354053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f7bbde4-b414-403a-af57-2cbab64d719f · outbound

This paper cites MetaNO: How to transfer your knowledge on learning hidden physics.Computer Methods in Applied Mechanics and Engineering, 417:116280, 2023.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance MetaNO: How to transfer your knowledge on learning hidden physics.Computer Methods in Applied Mechanics and Engineering, 417:116280, 2023

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:38.502651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96a3666d-1dc5-4d1a-8808-dba3c86c061d · outbound

This paper cites Alias-free mamba neural operator.

ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance Alias-free mamba neural operator

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-04T18:48:38.640981Z

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