Paper Citation Record · LEDGER

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2505.03021.

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pith.paper-citation-record.v1
2505.03021 v1

Coverage vector

measured 32 of 32 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation 62387d8e-33f2-4706-95ba-adf481c532e8 · outbound

This paper cites Training material models using gradient descent algorithms.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Training material models using gradient descent algorithms

Reference 1

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Observation 7f61bc4d-cc93-4b23-98d1-52102ede61b6 · outbound

This paper cites An advancement in cyclic plasticity modeling for multiaxial ratcheting simulation,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs An advancement in cyclic plasticity modeling for multiaxial ratcheting simulation,

Reference 2

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Observation 88687193-8c03-4c59-aaee-4c2eb02af7c6 · outbound

This paper cites Anatomy of coupled constitutive models for ratcheting simulation,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Anatomy of coupled constitutive models for ratcheting simulation,

Reference 3

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Observation d0d68a77-bb41-4559-b4e2-1a3d2aac2dfa · outbound

This paper cites Implicit constitutive modelling for viscoplasticity using neural networks,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Implicit constitutive modelling for viscoplasticity using neural networks,

Reference 4

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This paper cites Development of LSTM networks for predicting viscoplasticity with effects of deformation, strain rate, and temperature history,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Development of LSTM networks for predicting viscoplasticity with effects of deformation, strain rate, and temperature history,

Reference 5

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Observation 7cc94e3a-176d-4da8-956f-8615570408e1 · outbound

This paper cites Numerical characterisation of uncured elastomers by a neural network based approach,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Numerical characterisation of uncured elastomers by a neural network based approach,

Reference 6

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This paper cites Deep learning predicts path-dependent plasticity,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Deep learning predicts path-dependent plasticity,

Reference 7

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A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Unresolved cited work

Reference 8

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Observation 99169dfa-723c-4151-91db-4bdb35b36154 · outbound

This paper cites Elastoplastic constitutive modeling under the complex loading driven by GRU and small-amount data,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Elastoplastic constitutive modeling under the complex loading driven by GRU and small-amount data,

Reference 9

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Observation f69c74e4-b39e-4e02-8e9e-ace032ea804f · outbound

This paper cites Deep residual learning for image recognition,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Deep residual learning for image recognition,

Reference 10

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Observation 249dec90-707a-484d-bb3f-ef73e681ee89 · outbound

This paper cites Neural ordinary differential equations,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Neural ordinary differential equations,

Reference 11

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This paper cites Equivariant Flows: sampling configurations for multi-body systems with symmetric energies.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Equivariant Flows: sampling configurations for multi-body systems with symmetric energies

Reference 12

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This paper cites Latent ordinary differential equations for irregularly-sampled time series,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Latent ordinary differential equations for irregularly-sampled time series,

Reference 13

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Observation 3c52f8ff-de6c-481f-84b5-f08a17a0c987 · outbound

This paper cites Scalable gradients and variational inference for stochastic differential equations,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Scalable gradients and variational inference for stochastic differential equations,

Reference 14

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Observation 155881f4-936c-4f4a-9dc7-ccefcd461567 · outbound

This paper cites Training material models using gradient descent algorithms,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Training material models using gradient descent algorithms,

Reference 15

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Observation a20a1364-6677-4618-adad-50c120f3e8c6 · outbound

This paper cites Automatic differentiation in pytorch,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Automatic differentiation in pytorch,

Reference 16

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Observation 53851318-071c-4ecf-87ba-0fe993b7e025 · outbound

This paper cites Hierarchical deep -learning neural networks: finite elements and beyond,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Hierarchical deep -learning neural networks: finite elements and beyond,

Reference 17

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This paper cites HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis,

Reference 18

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This paper cites Convolution Hierarchical Deep-Learning Neural Network Tensor Decomposition (C- HiDeNN-TD) for high -resolution topology optimization,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Convolution Hierarchical Deep-Learning Neural Network Tensor Decomposition (C- HiDeNN-TD) for high -resolution topology optimization,

Reference 19

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Observation 3d8bb3ee-e3e0-4181-9549-9173522b28b1 · outbound

This paper cites Semi -supervised invertible neural operators for Bayesian inverse problems,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Semi -supervised invertible neural operators for Bayesian inverse problems,

Reference 20

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This paper cites Information processing, data inferences, and scientific generalization,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Information processing, data inferences, and scientific generalization,

Reference 21

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This paper cites BACON. 5: The discovery of conservation laws,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs BACON. 5: The discovery of conservation laws,

Reference 22

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This paper cites Genetic programming as a means for programming computers by natural selection,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Genetic programming as a means for programming computers by natural selection,

Reference 23

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This paper cites On the limited memory BFGS method for large scale optimization,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs On the limited memory BFGS method for large scale optimization,

Reference 24

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Observation 26d9fff4-8329-4ae2-8ab0-bbb6eac0090d · outbound

This paper cites Training material models using gradient descent algorithms,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Training material models using gradient descent algorithms,

Reference 25

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This paper cites Identity mappings in deep residual networks,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Identity mappings in deep residual networks,

Reference 26

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Observation 8ec39d4a-ca43-459b-905f-09beef881117 · outbound

This paper cites Delving deep into rectifiers: Surpassing human -level performance on imagenet classification,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Delving deep into rectifiers: Surpassing human -level performance on imagenet classification,

Reference 27

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This paper cites Reference constitutive model for Alloy 617 and 316H stainless steel for use with the ASME Division 5 design by inelastic analysis rules,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Reference constitutive model for Alloy 617 and 316H stainless steel for use with the ASME Division 5 design by inelastic analysis rules,

Reference 28

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This paper cites Gaussian Error Linear Units (GELUs).

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Gaussian Error Linear Units (GELUs)

Reference 29

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This paper cites Adam: A Method for Stochastic Optimization.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Adam: A Method for Stochastic Optimization

Reference 30

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This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:07:11.640800Z

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Observation 2a3156f2-b3d0-4876-9259-6b4e35ea76b5 · outbound

This paper cites PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data.

A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data

Reference 32

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