History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials | Paper Citation Record

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This paper cites Deep learning for plasticity and thermo-viscoplasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Deep learning for plasticity and thermo-viscoplasticity

Reference 1

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This paper cites Distance-preserving manifold denoising for data-driven mechanics.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Distance-preserving manifold denoising for data-driven mechanics

Reference 2

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This paper cites A data-driven approach for plasticity using history surrogates: Theory and application in the context of truss structures.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A data-driven approach for plasticity using history surrogates: Theory and application in the context of truss structures

Reference 3

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This paper cites Scheduled sampling for sequence prediction with recurrent neural networks.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Scheduled sampling for sequence prediction with recurrent neural networks

Reference 4

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This paper cites From cp-fft to cp-rnn: Recurrent neural network surrogate model of crystal plasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials From cp-fft to cp-rnn: Recurrent neural network surrogate model of crystal plasticity

Reference 5

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This paper cites On the importance of self-consistency in recurrent neural network models representing elasto-plastic solids.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials On the importance of self-consistency in recurrent neural network models representing elasto-plastic solids

Reference 6

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This paper cites Data-driven frac- ture mechanics.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Data-driven frac- ture mechanics

Reference 7

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This paper cites Data-driven rate-dependent fracture mechanics.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Data-driven rate-dependent fracture mechanics

Reference 8

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This paper cites Neural ordinary differential equations.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Neural ordinary differential equations

Reference 9

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This paper cites Thermodynamics with internal state variables.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Thermodynamics with internal state variables

Reference 10

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This paper cites Neural networks for constitutive modeling: From universal function approximators to advanced models and the integration of physics.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Neural networks for constitutive modeling: From universal function approximators to advanced models and the integration of physics

Reference 11

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This paper cites Differentiable neural-integrated meshfree method for forward and inverse modeling of finite strain hyperelasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Differentiable neural-integrated meshfree method for forward and inverse modeling of finite strain hyperelasticity

Reference 12

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This paper cites Model-free data-driven inelasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Model-free data-driven inelasticity

Reference 13

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This paper cites Convex neural networks learn generalized standard material models.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Convex neural networks learn generalized standard material models

Reference 14

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This paper cites Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:06.120273Z

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This paper cites Dnn2: A hyper- parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Dnn2: A hyper- parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions

Reference 16

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This paper cites A review on data-driven constitutive laws for solids.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A review on data-driven constitutive laws for solids

Reference 17

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This paper cites Data-driven constitutive models for brittle solids displaying progressive anisotropic damage.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Data-driven constitutive models for brittle solids displaying progressive anisotropic damage

Reference 18

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This paper cites Knowledge-based modeling of material behavior with neural networks.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Knowledge-based modeling of material behavior with neural networks

Reference 19

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This paper cites Deep Learning.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Deep Learning

Reference 20

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This paper cites Deep learning.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Deep learning

Reference 21

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This paper cites On the potential of recurrent neural networks for modeling path dependent plasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials On the potential of recurrent neural networks for modeling path dependent plasticity

Reference 22

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This paper cites Constitutive model character- ization and discovery using physics-informed deep learning.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Constitutive model character- ization and discovery using physics-informed deep learning

Reference 23

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This paper cites Failure criteria for unidirectional fiber composites.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Failure criteria for unidirectional fiber composites

Reference 24

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This paper cites Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Sequential deep operator networks (s-deeponet) for predicting full-field solutions under time-dependent loads

Reference 25

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This paper cites A physics-constrained data-driven approach based on locally convex reconstruction for noisy database.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A physics-constrained data-driven approach based on locally convex reconstruction for noisy database

Reference 26

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This paper cites Manifold learning based data-driven modeling for soft biological tissues.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Manifold learning based data-driven modeling for soft biological tissues

Reference 27

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This paper cites A hybrid deep neural operator/finite element method for ice-sheet modeling.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A hybrid deep neural operator/finite element method for ice-sheet modeling

Reference 28

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This paper cites Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials

Reference 29

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This paper cites Deep autoencoders for physics-constrained data-driven nonlinear materials modeling.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Deep autoencoders for physics-constrained data-driven nonlinear materials modeling

Reference 30

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This paper cites Machine learning based modeling of path- dependent materials for finite element analysis.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Machine learning based modeling of path- dependent materials for finite element analysis

Reference 31

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This paper cites Incremental neural controlled differential equations for modeling of path-dependent material behavior.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Incremental neural controlled differential equations for modeling of path-dependent material behavior

Reference 32

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This paper cites Historical review of internal state variable theory for inelasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Historical review of internal state variable theory for inelasticity

Reference 33

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This paper cites A machine learning based plasticity model using proper orthogonal decomposition.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A machine learning based plasticity model using proper orthogonal decomposition

Reference 34

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This paper cites A neural ordinary differential equation framework for modeling inelastic stress response via internal state variables.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A neural ordinary differential equation framework for modeling inelastic stress response via internal state variables

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:34:00.570642Z

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This paper cites Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

Reference 36

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This paper cites Neural controlled differ- ential equations for irregular time series.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Neural controlled differ- ential equations for irregular time series

Reference 37

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This paper cites Data-driven computational mechanics.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Data-driven computational mechanics

Reference 38

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Observation 3f42770c-0d63-4dc8-aff1-2c1b3a3de38e · outbound

This paper cites Polyconvex anisotropic hyperelasticity with neural networks.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Polyconvex anisotropic hyperelasticity with neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:59.371715Z

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This paper cites Neural operator: Learning maps between function spaces with applications to pdes.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Neural operator: Learning maps between function spaces with applications to pdes

Reference 40

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This paper cites Artificial neural network as an incremental non-linear constitutive model for a finite element code.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Artificial neural network as an incremental non-linear constitutive model for a finite element code

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:59.103774Z

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This paper cites Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:58.796598Z

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Observation 85591e2f-fad4-4e2b-93b9-b9a78db5c8d6 · outbound

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

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Fourier Neural Operator for Parametric Partial Differential Equations

Reference 43

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

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 44

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This paper cites Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:58.459648Z

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History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Unresolved cited work

Reference 46

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

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 47

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This paper cites Physically recurrent neural networks for path-dependent heterogeneous materials: Embedding constitutive models in a data-driven surrogate.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Physically recurrent neural networks for path-dependent heterogeneous materials: Embedding constitutive models in a data-driven surrogate

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:57.516124Z

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Observation 542b9c93-a36f-4f4c-83cb-9e92da4c2588 · outbound

This paper cites Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (tann).

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (tann)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:57.211494Z

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Observation e868fbfb-3c5f-465e-9d81-4b12810205b4 · outbound

This paper cites Evolution tann and the identification of internal variables and evolution equations in solid mechanics.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Evolution tann and the identification of internal variables and evolution equations in solid mechanics

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:56.899695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ff8c3d3c-442f-4e53-b371-74750be19e18 · outbound

This paper cites Thermodynamics-based artificial neural networks for constitutive modeling.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Thermodynamics-based artificial neural networks for constitutive modeling

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:56.605395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e2eb81c5-c24e-45d6-a578-098ab783d3c9 · outbound

This paper cites Curriculum dropout, in: Proceedings of the IEEE international conference on computer vision, pp.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Curriculum dropout, in: Proceedings of the IEEE international conference on computer vision, pp

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:56.350356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d5ea624e-59ed-4007-b6d9-af50e8520de3 · outbound

This paper cites an unresolved cited work.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:33:56.067191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d404de08-fe8a-42f0-ba36-26dd022a91fe · outbound

This paper cites A data-driven approach to nonlinear elasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A data-driven approach to nonlinear elasticity

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:55.405378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d4009c0d-449d-4da5-95aa-807046092758 · outbound

This paper cites Linear attention coupled fourier neural operator for simulation of three-dimensional turbulence.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Linear attention coupled fourier neural operator for simulation of three-dimensional turbulence

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:55.085381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 19fe5d07-3074-4184-9dfe-67dc48965f26 · outbound

This paper cites Attention-enhanced neural network models for turbulence simulation.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Attention-enhanced neural network models for turbulence simulation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:54.766297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e1ac7825-6da8-4b3d-b94f-5b731aca2aa6 · outbound

This paper cites an unresolved cited work.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:33:54.424812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 03e4348d-8556-4478-92c4-09ccd88fe117 · outbound

This paper cites A compar- ative study on different neural network architectures to model inelasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A compar- ative study on different neural network architectures to model inelasticity

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:54.137803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 28c45446-9d19-4860-b498-edc431f27254 · outbound

This paper cites Learning to simulate complex physics with graph networks, in: International conference on machine learning, PMLR.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Learning to simulate complex physics with graph networks, in: International conference on machine learning, PMLR

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:53.827251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7b7a42ac-d7e5-4597-8ddf-6d3a637d7869 · outbound

This paper cites The mechanics and thermodynamics of continuous media.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials The mechanics and thermodynamics of continuous media

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:53.468748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation af54d542-761a-4f8d-a7d4-71896c4a0f88 · outbound

This paper cites Computational inelasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Computational inelasticity

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:53.218916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f9d5900f-961b-44b0-bd8f-febfd0a47b64 · outbound

This paper cites Consistent tangent operators for rate- independent elastoplasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Consistent tangent operators for rate- independent elastoplasticity

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:52.952132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ca875166-90c8-4220-a554-3cb2f8297d15 · outbound

This paper cites A novel self- adversarial training scheme for enhanced robustness of inelastic constitutive descriptions by neural networks.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A novel self- adversarial training scheme for enhanced robustness of inelastic constitutive descriptions by neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:52.686371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a463563f-cccd-418b-9071-ebf958efea37 · outbound

This paper cites Nn-euclid: Deep-learning hyperelasticity without stress data.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Nn-euclid: Deep-learning hyperelasticity without stress data

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:52.406757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 758fccc7-31af-4918-8ad8-a3c067892378 · outbound

This paper cites Rational mechanics of materials.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Rational mechanics of materials

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:52.103614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d64fbd47-9035-4bd7-a22d-a436871ececc · outbound

This paper cites Attention is all you need.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Attention is all you need

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:51.849675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6042b0a5-7920-430d-83a7-bf54b67e2d28 · outbound

This paper cites Geometric learning for computational me- chanics part ii: Graph embedding for interpretable multiscale plasticity.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Geometric learning for computational me- chanics part ii: Graph embedding for interpretable multiscale plasticity

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:51.582988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c48c4cdc-a1d3-46ed-bfc7-231439148487 · outbound

This paper cites A deep learning framework for constitutive modeling based on temporal convolutional network.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A deep learning framework for constitutive modeling based on temporal convolutional network

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:51.271913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c70d4f7f-c566-41d1-82a0-91b438a23599 · outbound

This paper cites U-fno—an enhanced fourier neural operator-based deep-learning model for multiphase flow.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials U-fno—an enhanced fourier neural operator-based deep-learning model for multiphase flow

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:50.985832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 50a9ae64-efe2-49ab-b1f0-36a786721ea5 · outbound

This paper cites A recurrent neural network- accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials A recurrent neural network- accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:50.727852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cf538e89-a3a1-46b4-8263-7454bf577f91 · outbound

This paper cites Jax-fem: A differentiable gpu-accelerated 3d finite element solver for au- tomatic inverse design and mechanistic data science.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Jax-fem: A differentiable gpu-accelerated 3d finite element solver for au- tomatic inverse design and mechanistic data science

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:50.413694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0d538a6e-17e7-4e34-b71f-1897e1cf0709 · outbound

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

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Learning deep implicit fourier neural operators (ifnos) with applications to heterogeneous material modeling

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:50.133411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4872b14a-34f2-45c4-9ffe-2cac4eef3ce6 · outbound

This paper cites Operator learning for homogenizing hypere- lastic materials, without pde data.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Operator learning for homogenizing hypere- lastic materials, without pde data

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T04:33:49.569153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e952a1f7-3e95-4ab5-94b0-747753bd8087 · outbound

This paper cites Proceedings of the National Academy of Sciences 116, 26414–26420.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Proceedings of the National Academy of Sciences 116, 26414–26420

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:55.699814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9fa2a547-9797-42c6-83d8-022b26f8d924 · outbound

This paper cites Journal of the Mechanics and Physics of Solids 178, 105329.

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials Journal of the Mechanics and Physics of Solids 178, 105329

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:33:57.851675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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