Paper Citation Record · LEDGER

Equilibrium Conserving Neural Operators for Super-Resolution Learning

As of 24 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 1 inbound Pith citation observation for arXiv:2504.13422.

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

pith.paper-citation-record.v1
2504.13422 v1

Coverage vector

measured 66 of 66 reference resolution

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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: pith, observed 2026-08-16T10:13:04.096759Z

Reference resolution

66 of 66 outbound references displayed

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Outbound references

Observation 97f2702c-a94a-4520-ae53-5dfbbc981c7e · outbound

This paper cites Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications

Reference 1

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Observation f3eb5f00-b022-484b-ac8e-619228924ac5 · outbound

This paper cites Crystal plasticity simulation study on the influence of texture on earing in steel.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Crystal plasticity simulation study on the influence of texture on earing in steel

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:24.203565Z

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Observation e7cbca37-93df-44bb-aeda-3b3b8ad0371f · outbound

This paper cites The Finite Element Method: Linear Static and Dynamic Finite Element Analysis.

Equilibrium Conserving Neural Operators for Super-Resolution Learning The Finite Element Method: Linear Static and Dynamic Finite Element Analysis

Reference 3

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Equilibrium Conserving Neural Operators for Super-Resolution Learning Unresolved cited work

Reference 4

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Observation 45f6a3ac-73f0-4edb-b088-7436323fa79a · outbound

This paper cites Green algorithms: quantifying the carbon footprint of computation.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Green algorithms: quantifying the carbon footprint of computation

Reference 5

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Observation c0ac9be0-d64d-4717-81db-d79282148623 · outbound

This paper cites MPI+X: task-based parallelisation and dynamic load balance of finite element assembly.

Equilibrium Conserving Neural Operators for Super-Resolution Learning MPI+X: task-based parallelisation and dynamic load balance of finite element assembly

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:24.142959Z

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Observation aee00671-d3cf-4379-a2dc-a28c7471c57f · outbound

This paper cites A heterogeneous parallel model of unstructured mesh finite element method based on CPU+GPU.Highlights in Science, Engineering and Technology, 77:173–178, 2023.

Equilibrium Conserving Neural Operators for Super-Resolution Learning A heterogeneous parallel model of unstructured mesh finite element method based on CPU+GPU.Highlights in Science, Engineering and Technology, 77:173–178, 2023

Reference 7

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Observation 44472931-4e44-46d2-bf8c-18f12f97b1bd · outbound

This paper cites Continuum Scale Simulation of Engineering Materials: Fundamentals – Microstructures – Process Applications.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Continuum Scale Simulation of Engineering Materials: Fundamentals – Microstructures – Process Applications

Reference 8

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Observation 97fc2ff2-06c1-40ed-be38-6137fdc18fcb · outbound

This paper cites Microstructure-sensitive computational structure-property relations in materials design.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Microstructure-sensitive computational structure-property relations in materials design

Reference 9

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This paper cites Microstructure sensitive design for performance optimization.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Microstructure sensitive design for performance optimization

Reference 10

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Observation 1461f2ca-491a-424b-b800-f5ec38f68aad · outbound

This paper cites Neural operators for accelerating scientific simulations and design.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Neural operators for accelerating scientific simulations and design

Reference 11

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Observation 33e42af4-1325-4ce2-a084-32a282f4cf2b · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 12

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

Equilibrium Conserving Neural Operators for Super-Resolution Learning Fourier Neural Operator for Parametric Partial Differential Equations

Reference 13

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Observation bf02e7a9-7227-4d0a-8572-3fab903c5249 · outbound

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

Equilibrium Conserving Neural Operators for Super-Resolution Learning Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Reference 14

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Observation f6d1a3c1-0f1d-4a07-9b62-72d76e2441d8 · outbound

This paper cites Laplace neural operator for solving differential equations.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Laplace neural operator for solving differential equations

Reference 15

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Observation 9bff4893-d0dc-4d3f-93db-e0a03b71664e · outbound

This paper cites Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems

Reference 16

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Observation 9fdc1283-c31d-429b-a033-810f9a334cea · outbound

This paper cites Towards Multi-spatiotemporal-scale Generalized PDE Modeling.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Towards Multi-spatiotemporal-scale Generalized PDE Modeling

Reference 17

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Observation 87384f5d-fc58-43d6-8dd4-b478ff5caba1 · outbound

This paper cites Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO).

Equilibrium Conserving Neural Operators for Super-Resolution Learning Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)

Reference 18

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Observation 4fec60fc-1101-473b-844b-247b175a99e1 · outbound

This paper cites Thermodynamically-Informed Iterative Neural Operators for Heterogeneous Elastic Localization.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Thermodynamically-Informed Iterative Neural Operators for Heterogeneous Elastic Localization

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-16T12:16:23.435077Z

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Observation 4942e0ec-3227-4c3b-90b9-3041311f1b12 · outbound

This paper cites Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:24.007524Z

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Observation f04cdb80-74ea-4a46-8932-952869680878 · outbound

This paper cites Recurrent localization networks applied to the lippmann-schwinger equation.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Recurrent localization networks applied to the lippmann-schwinger equation

Reference 21

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Observation dc935cf1-56af-4fd7-8225-2429dddb5df4 · outbound

This paper cites Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network

Reference 22

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Observation 1614d0b1-83ca-4502-83f6-1ac89f37322e · outbound

This paper cites Computational Methods for Microstructure-Property Relationships, volume.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Computational Methods for Microstructure-Property Relationships, volume

Reference 23

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Observation 6888c4c8-1578-4f7b-ad8c-baa7fed682f6 · outbound

This paper cites An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials.

Equilibrium Conserving Neural Operators for Super-Resolution Learning An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials

Reference 24

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Observation 7643e53b-2d89-40c2-9632-11617c86f47c · outbound

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

Equilibrium Conserving Neural Operators for Super-Resolution Learning Learning the solution operator of parametric partial differential equations with physics-informed DeepONets

Reference 25

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Equilibrium Conserving Neural Operators for Super-Resolution Learning Physics-informed neural operator for learning partial differential equations

Reference 26

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This paper cites Physics-informed deep neural operator networks.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Physics-informed deep neural operator networks

Reference 27

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Observation 1f01feb0-5aea-44d6-ae1f-02fe6655aac6 · outbound

This paper cites From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning.

Equilibrium Conserving Neural Operators for Super-Resolution Learning From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning

Reference 28

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This paper cites Multifidelity modeling for physics- informed neural networks (PINNs).

Equilibrium Conserving Neural Operators for Super-Resolution Learning Multifidelity modeling for physics- informed neural networks (PINNs)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.899211Z

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This paper cites Physics-informed deep learning for 3D modeling of light diffraction from optical metasurfaces.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Physics-informed deep learning for 3D modeling of light diffraction from optical metasurfaces

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.882023Z

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Observation f445864c-8b40-455b-8ecb-1df16764c47d · outbound

This paper cites On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks.

Equilibrium Conserving Neural Operators for Super-Resolution Learning On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.865645Z

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Observation 4c40c1e1-3df2-476d-a6c6-4ec060be7699 · outbound

This paper cites Learning a neural solver for parametric PDE to enhance physics-informed methods, 2024.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Learning a neural solver for parametric PDE to enhance physics-informed methods, 2024

Reference 32

Resolution: verified exact; raw_fallback, observed 2026-08-16T12:16:23.394125Z

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This paper cites Physics- informed neural networks with hard constraints for inverse design.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Physics- informed neural networks with hard constraints for inverse design

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.849638Z

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Observation 0dc967bb-4791-46aa-bd3b-bf48901d4511 · outbound

This paper cites Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations

Reference 34

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This paper cites Hamiltonian neural networks.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Hamiltonian neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.817792Z

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Observation facd458f-a327-4ece-9e85-3dffb9692684 · outbound

This paper cites Machine learning structure preserving brackets for forecasting irreversible processes.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Machine learning structure preserving brackets for forecasting irreversible processes

Reference 36

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This paper cites Structure-preserving sparse identification of nonlinear dynamics for data-driven modeling.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Structure-preserving sparse identification of nonlinear dynamics for data-driven modeling

Reference 37

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Observation ff341d0f-fe7c-49c2-9c1a-2db7d0ffe3a1 · outbound

This paper cites GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems.

Equilibrium Conserving Neural Operators for Super-Resolution Learning GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems

Reference 38

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Observation 2c90ac16-63a0-40e8-af3b-86187abe3dce · outbound

This paper cites Structure preserving neural networks and applications to optimal control problems.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Structure preserving neural networks and applications to optimal control problems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.766789Z

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Observation cad47845-4333-4540-b505-ab1885333ae7 · outbound

This paper cites Convolutional neural operators.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Convolutional neural operators

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.750761Z

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Observation 0d0f0cd1-6aa3-4374-8e6d-52b6aabcfc31 · outbound

This paper cites Universal physics transformers: A framework for efficiently scaling neural operators.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Universal physics transformers: A framework for efficiently scaling neural operators

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.735396Z

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Observation a39c986c-f856-49f4-8898-8be29f2999a8 · outbound

This paper cites A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures.

Equilibrium Conserving Neural Operators for Super-Resolution Learning A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.060698Z

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Observation 4cc8d3f2-4a95-4d2d-bb77-f32de2fca7e2 · outbound

This paper cites Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.065563Z

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Observation 09283232-5317-4258-aef6-7c0124ab0cea · outbound

This paper cites McDowell.

Equilibrium Conserving Neural Operators for Super-Resolution Learning McDowell

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.719848Z

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Observation f260eb59-fbb2-495a-8100-47c58c526ada · outbound

This paper cites When and why PINNs fail to train: A neural tangent kernel perspective.

Equilibrium Conserving Neural Operators for Super-Resolution Learning When and why PINNs fail to train: A neural tangent kernel perspective

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.075678Z

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Observation 5e912cea-061a-4a67-91ed-1210b06915ca · outbound

This paper cites Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Multi-Grid Tensorized Fourier Neural Operator for High-Resolution PDEs

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.080271Z

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Observation ccea45a6-23a7-45a2-adcf-bd362462f09a · outbound

This paper cites Simulated microstructure-sensitive extreme value probabilities for high cycle fatigue of duplex Ti–6Al–4V.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Simulated microstructure-sensitive extreme value probabilities for high cycle fatigue of duplex Ti–6Al–4V

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.694781Z

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Observation deb9e426-730f-4f80-93e6-5307d6d9eaac · outbound

This paper cites Physics- informed machine learning.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Physics- informed machine learning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.089318Z

Source-reported events for the cited work

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Observation 403862d0-2924-4a51-aa64-8ca15e092f54 · outbound

This paper cites Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.668946Z

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Observation dd7b2adf-ba5a-439f-b2e6-1947bd2f43bb · outbound

This paper cites A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids.

Equilibrium Conserving Neural Operators for Super-Resolution Learning A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.098532Z

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Observation f44660fc-abcc-47f5-a1f8-9c237a3bd560 · outbound

This paper cites Denoising diffusion probabilistic models.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Denoising diffusion probabilistic models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.103486Z

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Observation c65d0825-6a46-4721-9c0e-59b710b84da7 · outbound

This paper cites Statistically conditioned polycrystal generation using denoising diffusion models.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Statistically conditioned polycrystal generation using denoising diffusion models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.642873Z

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.

Observation ebcf8248-1ff3-4d50-9e66-cf5d059d04b7 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Adam: A Method for Stochastic Optimization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.112780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d13c2dd-06a8-4eea-84bc-874664e89dfa · outbound

This paper cites Microstructure reconstruction of 2D/3D random materials via diffusion-based deep generative models.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Microstructure reconstruction of 2D/3D random materials via diffusion-based deep generative models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.627103Z

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.

Observation 41230a7c-7c1d-490e-b610-6d4b6a8d52bd · outbound

This paper cites Generating 3D images of material microstructures from a single 2D image: a denoising diffusion approach.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Generating 3D images of material microstructures from a single 2D image: a denoising diffusion approach

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.612011Z

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.

Observation 84072ec5-81a6-4a07-a1c5-75e3ca42407b · outbound

This paper cites A review of activation function for artificial neural network.

Equilibrium Conserving Neural Operators for Super-Resolution Learning A review of activation function for artificial neural network

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.596564Z

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.

Observation 077b6953-08a0-453c-a229-86ec13ba1c9b · outbound

This paper cites Decoupled Weight Decay Regularization.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Decoupled Weight Decay Regularization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.132251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bc5a6ba-2c43-4afe-aaac-3f5d565c22e5 · outbound

This paper cites High-performance large-scale image recognition without normalization.

Equilibrium Conserving Neural Operators for Super-Resolution Learning High-performance large-scale image recognition without normalization

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.136786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c210884-a0e7-4478-8625-68175c1a45ce · outbound

This paper cites On the Convergence of Adam and Beyond.

Equilibrium Conserving Neural Operators for Super-Resolution Learning On the Convergence of Adam and Beyond

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.141341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aa2a9a0-c140-4e4a-89f8-e1584f1ab354 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Optuna: A next-generation hyperparameter optimization framework

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.146367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2533dc65-29f2-4b3f-bd61-4751ec28d048 · outbound

This paper cites Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T12:16:23.151207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce943219-8410-4ad9-9f6d-28e741d661bb · outbound

This paper cites An elasto-viscoplastic formulation based on fast Fourier transforms for the prediction of micromechanical fields in polycrystalline materials.

Equilibrium Conserving Neural Operators for Super-Resolution Learning An elasto-viscoplastic formulation based on fast Fourier transforms for the prediction of micromechanical fields in polycrystalline materials

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.559793Z

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.

Observation 064bb34c-a00a-4c40-9fa0-c466235ef8c8 · outbound

This paper cites an unresolved cited work.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-16T12:16:23.544242Z

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.

Observation 49e7b39e-5281-4953-91df-646544392193 · outbound

This paper cites Groeber and Michael A.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Groeber and Michael A

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.528696Z

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.

Observation b04ca989-f9fb-4eeb-b619-b153341ebcc1 · outbound

This paper cites Robertson, Daniel Diaz, Coleman Alleman, Zhen Zhang, Anthony D.

Equilibrium Conserving Neural Operators for Super-Resolution Learning Robertson, Daniel Diaz, Coleman Alleman, Zhen Zhang, Anthony D

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.513419Z

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.

Observation 0ace2888-9dad-46a1-9365-e6b66a881950 · outbound

This paper cites medium”|“high.

Equilibrium Conserving Neural Operators for Super-Resolution Learning medium”|“high

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:16:23.497758Z

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.

Pith citing papers

Observation 50e96d94-d7fc-40dd-9e94-57b16ee33027 · inbound

Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials cites this paper.

Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials Equilibrium Conserving Neural Operators for Super-Resolution Learning

Reference 29

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T10:13:04.112723Z

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.