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Paper Citation Record · LEDGER

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials

As of 19 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.16918.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.16918 v1

Coverage vector

measured 31 of 31 reference resolution

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measured 31 of 31 standing notices

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation e98a8ea4-f7d6-4b83-a7da-026fcaebbac9 · outbound

This paper cites Model Reduction and Neural Networks for Parametric PDEs.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Model Reduction and Neural Networks for Parametric PDEs

Reference 1

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This paper cites Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

Reference 2

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This paper cites FE-LSTM: A hybrid approach to accelerate multiscale simulations of architectured materials using Recurrent Neural Networks and Finite Element Analysis.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials FE-LSTM: A hybrid approach to accelerate multiscale simulations of architectured materials using Recurrent Neural Networks and Finite Element Analysis

Reference 3

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This paper cites FE2 Computations with Deep Neural Networks: Algorithmic Structure, Data Gener- ation, and Implementation.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials FE2 Computations with Deep Neural Networks: Algorithmic Structure, Data Gener- ation, and Implementation

Reference 4

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Observation 3a466459-6393-40c3-82bf-ba8ae6f8ef5f · outbound

This paper cites Nonlinear model reduction for operator learning.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Nonlinear model reduction for operator learning

Reference 5

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This paper cites Equino: A physics-informed neural operator for multiscale simulations.arXiv preprint arXiv:2504.07976, 2025.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Equino: A physics-informed neural operator for multiscale simulations.arXiv preprint arXiv:2504.07976, 2025

Reference 6

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Observation 0576bc86-79cc-44d8-859a-632879b258d0 · outbound

This paper cites Multiscale FE2 elastoviscoplastic analysis of composite structures.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Multiscale FE2 elastoviscoplastic analysis of composite structures

Reference 7

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This paper cites A remark on the application of the Newton-Raphson method in non- linear finite element analysis.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials A remark on the application of the Newton-Raphson method in non- linear finite element analysis

Reference 8

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A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Unresolved cited work

Reference 9

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Observation d20485f7-fa03-4ea4-bcdf-e43e4c3e51c4 · outbound

This paper cites Physics informed neural networks for continuum micromechanics.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Physics informed neural networks for continuum micromechanics

Reference 10

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This paper cites Generalizing universal function approximators.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Generalizing universal function approximators

Reference 11

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Observation ba66c54d-8605-4fbe-8550-5bc6e3589fe9 · outbound

This paper cites Kochmann, Jonathan B.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Kochmann, Jonathan B

Reference 12

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This paper cites On universal approximation and error bounds for Fourier Neural Operators.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials On universal approximation and error bounds for Fourier Neural Operators

Reference 13

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This paper cites Combining Physics-based and Data-driven Modeling for Building Energy Systems.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Combining Physics-based and Data-driven Modeling for Building Energy Systems

Reference 14

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This paper cites In vitro and in vivo study of additive manufactured porous Ti6Al4V scaffolds for repairing bone defects.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials In vitro and in vivo study of additive manufactured porous Ti6Al4V scaffolds for repairing bone defects

Reference 15

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This paper cites DeepONet: Learning nonlinear oper- ators for identifying differential equations based on the universal approximation theorem of operators, October 2019.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials DeepONet: Learning nonlinear oper- ators for identifying differential equations based on the universal approximation theorem of operators, October 2019

Reference 16

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

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Reference 17

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This paper cites A comprehensive and fair comparison of two neural operators (with practical extensions) based on F AIR data.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials A comprehensive and fair comparison of two neural operators (with practical extensions) based on F AIR data

Reference 18

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A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Unresolved cited work

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A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Patel, Nathaniel A

Reference 20

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This paper cites Rokoˇ s, R.H.J.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Rokoˇ s, R.H.J

Reference 22

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A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Kalina, J¨ org Brummund, and Markus K¨ astner

Reference 23

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This paper cites Kalina, J¨ org Brummund, WaiChing Sun, and Markus K¨ astner.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Kalina, J¨ org Brummund, WaiChing Sun, and Markus K¨ astner

Reference 24

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A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Staub, H

Reference 25

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A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Tikarrouchine, G

Reference 26

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This paper cites Iterated learning and multiscale modeling of history-dependent architectured metamaterials.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials Iterated learning and multiscale modeling of history-dependent architectured metamaterials

Reference 27

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This paper cites doi: 10.1016/j.ijsolstr.2019.01.018.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials doi: 10.1016/j.ijsolstr.2019.01.018

Reference 2019

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Reference 2020

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A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials doi: 10.3390/mca28040091

Reference 2023

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This paper cites doi: 10.1016/j.apenergy.2025.125853.

A Neural Operator based Hybrid Microscale Model for Multiscale Simulation of Rate-Dependent Materials doi: 10.1016/j.apenergy.2025.125853

Reference 2025

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