Paper Citation Record · LEDGER

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.24578.

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2505.24578 v1

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measured 40 of 40 reference resolution

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Outbound references

Observation 5184fa11-0d68-4b9f-8b09-373914f31005 · outbound

This paper cites Piezoelec- tric materials for controlling electro-chemical processes,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Piezoelec- tric materials for controlling electro-chemical processes,

Reference 1

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Observation 2eb68d29-a356-4949-9f62-98c8f30e0cc6 · outbound

This paper cites Piezoelectric materials for sus- tainable building structures: Fundamentals and applications,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Piezoelectric materials for sus- tainable building structures: Fundamentals and applications,

Reference 2

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This paper cites A review on applications of piezoelectric materials in aerospace industry,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems A review on applications of piezoelectric materials in aerospace industry,

Reference 3

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This paper cites Applications of piezoelectric materials in biomedical engineering,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Applications of piezoelectric materials in biomedical engineering,

Reference 4

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This paper cites A review on piezoelectric materials and their appli- cations,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems A review on piezoelectric materials and their appli- cations,

Reference 5

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Observation 096c7b4e-1b6f-422d-8aad-c3440957352f · outbound

This paper cites Hysteresis in piezoelectric and ferroelectric materials,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Hysteresis in piezoelectric and ferroelectric materials,

Reference 6

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Observation 063aa39c-015a-4e76-b0fa-c3aeed78a7e1 · outbound

This paper cites Adaptive estimated inverse output-feedback quantized control for piezoelectric positioning stage,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Adaptive estimated inverse output-feedback quantized control for piezoelectric positioning stage,

Reference 7

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Observation 717bc429-2f04-42f7-87df-106f3de9d65b · outbound

This paper cites Piezoelectric hysteresis analysis and loss separation,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Piezoelectric hysteresis analysis and loss separation,

Reference 8

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Observation 564ebaf0-c3d1-479c-a186-feff3593b5e1 · outbound

This paper cites ¨Uber die magnetische nachwirkung,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems ¨Uber die magnetische nachwirkung,

Reference 9

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Observation 9d9a6c23-5013-4fff-b48d-939114b3670f · outbound

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Unresolved cited work

Reference 10

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Observation 7e5b11ef-1327-4cbc-bc48-5a52ca17c10b · outbound

This paper cites Mag- netic hysteresis modeling with neural operators,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Mag- netic hysteresis modeling with neural operators,

Reference 11

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Observation 8fa0a298-7c0a-4d77-84cf-5b616fda6721 · outbound

This paper cites A survey on hysteresis modeling, identification and control,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems A survey on hysteresis modeling, identification and control,

Reference 12

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This paper cites Deep learning,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Deep learning,

Reference 13

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This paper cites Long short-term memory,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Long short-term memory,

Reference 14

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Observation ef48bfa1-3ec7-4b7f-a8a5-b4823787811d · outbound

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

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 15

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Observation 7b2ad8b3-40f6-4827-bdc4-82184cd41690 · outbound

This paper cites Adaptive neural control for hysteretic nonlinear systems with hysteresis neural direct inverse compensator and its application,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Adaptive neural control for hysteretic nonlinear systems with hysteresis neural direct inverse compensator and its application,

Reference 16

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Observation 4673e52d-ffa8-4006-b6e5-c917dcf6a21f · outbound

This paper cites Generalizable models of magnetic hys- teresis via physics-aware recurrent neural networks,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Generalizable models of magnetic hys- teresis via physics-aware recurrent neural networks,

Reference 17

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Observation 325038ca-2b29-4c3c-86c6-014ec8438601 · outbound

This paper cites Characterizing nonlinear piezoelectric dynamics through deep neural operator learning,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Characterizing nonlinear piezoelectric dynamics through deep neural operator learning,

Reference 18

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This paper cites Neural operator: Learning maps be- tween function spaces with applications to PDEs,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Neural operator: Learning maps be- tween function spaces with applications to PDEs,

Reference 19

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This paper cites Neural operators for accelerating scientific simu- lations and design,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Neural operators for accelerating scientific simu- lations and design,

Reference 20

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Observation 3526865f-f15e-4444-b76e-ae0361d2994c · outbound

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

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators,

Reference 21

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

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Fourier neural operator for parametric partial differential equations,

Reference 22

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Convolutional neural operators for robust and accurate learning of PDEs,

Reference 23

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This paper cites Discovery of sparse hysteresis models for piezoelectric materials,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Discovery of sparse hysteresis models for piezoelectric materials,

Reference 24

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Observation ff9c8b88-c6d1-41dc-aa1e-09e5369fe25b · outbound

This paper cites Identifi- cation of Bouc–Wen hysteretic systems based on a joint optimization approach,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Identifi- cation of Bouc–Wen hysteretic systems based on a joint optimization approach,

Reference 25

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Observation 57940fbb-1184-4912-8042-32d5e31f38b6 · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed deep- onets,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Learning the solution operator of parametric partial differential equations with physics-informed deep- onets,

Reference 26

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Observation 25903808-025d-48e8-9c46-71a80b912bf1 · outbound

This paper cites Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers

Reference 27

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This paper cites Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML

Reference 28

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 29

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Discovering symbolic models from deep learn- ing with inductive biases,

Reference 30

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Observation d2396091-a6d7-4fc5-b16a-95b94df256bc · outbound

This paper cites Why are some hysteresis loops shaped like a butterfly?.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Why are some hysteresis loops shaped like a butterfly?

Reference 31

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Observation 8651bb50-79f9-4f02-a961-1d2af40f0245 · outbound

This paper cites Fourier neural operators for arbitrary resolution climate data downscaling,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Fourier neural operators for arbitrary resolution climate data downscaling,

Reference 32

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators,

Reference 33

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Data-driven discovery of partial differential equations,

Reference 34

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Regression shrinkage and selection via the lasso,

Reference 35

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This paper cites Ensemble- SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Ensemble- SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control,

Reference 36

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Observation bdcf4906-9d04-41f2-996d-6e10e6dc193c · outbound

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Derivative-based SINDy (DSINDy): Address- ing the challenge of discovering governing equations from noisy data,

Reference 37

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Observation f2b8a9d9-1749-4223-87f8-6b34217106cb · outbound

This paper cites Dynamic hysteresis model of grain-oriented ferromagnetic material using neural operators,.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Dynamic hysteresis model of grain-oriented ferromagnetic material using neural operators,

Reference 38

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Observation 243abd67-6335-495b-a451-46d4e01c46d5 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Adam: A Method for Stochastic Optimization

Reference 39

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Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems Unresolved cited work

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