Paper Citation Record · LEDGER

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks

As of 23 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2608.11447.

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pith.paper-citation-record.v1
2608.11447 v1

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measured 38 of 38 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

38 of 38 outbound references displayed

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Outbound references

Observation 0ea62718-8893-49b2-81e8-5b3bc2db9ff1 · outbound

This paper cites Polyurethane Composite Foams in High-Performance Applications: A Review,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Polyurethane Composite Foams in High-Performance Applications: A Review,

Reference 1

Resolution: verified exact; raw_fallback, observed 2026-08-15T14:16:41.913860Z

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Observation 02cb1e04-37d8-4cbd-b871-22af61cb47aa · outbound

This paper cites Redefining Construction: An In-Depth Review of Sustainable Polyurethane Applications,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Redefining Construction: An In-Depth Review of Sustainable Polyurethane Applications,

Reference 2

Resolution: verified exact; doi, observed 2026-08-15T14:16:41.466535Z

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Observation 3fd26152-a4a7-49ff-b265-e0ef554990ff · outbound

This paper cites Production of polyols and polyurethane from biomass: a review,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Production of polyols and polyurethane from biomass: a review,

Reference 3

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Observation efdab637-ebbb-448f-90b0-7d7cc5269dde · outbound

This paper cites Lignin‐based polyurethane: recent advances and future perspectives,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Lignin‐based polyurethane: recent advances and future perspectives,

Reference 4

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Observation 597e15fa-638c-40aa-b19e-b11af4caf7c0 · outbound

This paper cites Improved mechanical property, thermal performance, flame retardancy and fire behavior of lignin-based rigid polyurethane foam nanocomposite,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Improved mechanical property, thermal performance, flame retardancy and fire behavior of lignin-based rigid polyurethane foam nanocomposite,

Reference 5

Resolution: verified exact; doi, observed 2026-08-15T14:16:41.440261Z

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Observation 1a3e8267-e2b9-47dd-89d2-6f18c6b88f15 · outbound

This paper cites Lignin-derived bio-based flame retardants toward high-performance sustainable polymeric materials,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Lignin-derived bio-based flame retardants toward high-performance sustainable polymeric materials,

Reference 6

Resolution: verified exact; doi, observed 2026-08-15T14:16:41.425278Z

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Observation f6eb13ad-0bdf-4e68-9968-4826af24a300 · outbound

This paper cites Cell Morphology and Mechanical Properties of Rigid Polyurethane Foam,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Cell Morphology and Mechanical Properties of Rigid Polyurethane Foam,

Reference 7

Resolution: verified exact; doi, observed 2026-08-15T14:16:41.412586Z

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Observation 0662776b-a4f8-400b-ae0a-514b7969e903 · outbound

This paper cites Density, Microstructure, and Strain-Rate Effects on the Compressive Response of Polyurethane Foams,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Density, Microstructure, and Strain-Rate Effects on the Compressive Response of Polyurethane Foams,

Reference 8

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Observation 269e7344-9942-491d-9c65-7b623f504f6d · outbound

This paper cites Machine learning‐based model for predicting the material properties of nanostructured aerogels,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Machine learning‐based model for predicting the material properties of nanostructured aerogels,

Reference 9

Resolution: verified exact; doi, observed 2026-08-15T14:16:41.389966Z

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Observation 86c5deda-028c-4c16-83a3-60448bfe4c33 · outbound

This paper cites Applying machine learning for predicting thermal conductivity coefficient of polymeric aerogels,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Applying machine learning for predicting thermal conductivity coefficient of polymeric aerogels,

Reference 10

Resolution: verified exact; doi, observed 2026-08-15T14:16:41.378683Z

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Observation 75759a6a-1b8e-4330-aacb-f547774f0f76 · outbound

This paper cites A developed convolutional neural network model for accurately and stably predicting effective thermal conductivity of gradient porous ceramic materials,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks A developed convolutional neural network model for accurately and stably predicting effective thermal conductivity of gradient porous ceramic materials,

Reference 11

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Observation 979a140d-8b6f-492d-b46c-937ca3f7ca07 · outbound

This paper cites Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain,

Reference 12

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Observation 27e9b0fc-f7d6-495b-8b9c-e5941fe276eb · outbound

This paper cites Predicting 3D particles shapes based on 2D images by using convolutional neural network,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Predicting 3D particles shapes based on 2D images by using convolutional neural network,

Reference 13

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Observation 7c97f379-f74b-43a4-8541-2b5072cacec4 · outbound

This paper cites Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks,

Reference 14

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Observation 204a8c97-48a2-489e-940d-697fd18034f2 · outbound

This paper cites Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images,

Reference 15

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Observation 95b9b2b1-de1f-4b7d-b698-d45b373a2269 · outbound

This paper cites Data-driven prediction of the mechanical behavior of nanocrystalline graphene using a deep convolutional neural network with PCA,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Data-driven prediction of the mechanical behavior of nanocrystalline graphene using a deep convolutional neural network with PCA,

Reference 16

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Observation 6330a107-c58c-4986-9f76-7943a2c09b96 · outbound

This paper cites Neural network-optimized imaging for classifying lignin-based polyurethane foams: Linking molecular composition to cellular microstructure using advanced machine learning,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Neural network-optimized imaging for classifying lignin-based polyurethane foams: Linking molecular composition to cellular microstructure using advanced machine learning,

Reference 17

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Observation ab7d3689-bfc7-4bb4-a8b1-6e9c2dcaac48 · outbound

This paper cites Exploring the microstructure–property relationship in polymer foams using advanced statistical methods, machine learning and deep learning: A review,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Exploring the microstructure–property relationship in polymer foams using advanced statistical methods, machine learning and deep learning: A review,

Reference 18

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Observation 70b6e7d3-76d2-4c11-856b-a23b52e910eb · outbound

This paper cites Solid-State Structures and Properties of Lignin Hydrogenolysis Oil Compounds: Shedding a Unique Light on Lignin Valorization,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Solid-State Structures and Properties of Lignin Hydrogenolysis Oil Compounds: Shedding a Unique Light on Lignin Valorization,

Reference 19

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Observation fbc8b4c2-9673-472d-aba9-bc15b06963ac · outbound

This paper cites Preparation of Mechanically Robust Bio-Based Polyurethane Foams Using Depolymerized Native Lignin,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Preparation of Mechanically Robust Bio-Based Polyurethane Foams Using Depolymerized Native Lignin,

Reference 20

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Observation fd81fa24-c144-4d21-93e9-4d1622f55f59 · outbound

This paper cites Toward bio-based epoxy thermoset polymers from depolymerized native lignins produced at the pilot scale,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Toward bio-based epoxy thermoset polymers from depolymerized native lignins produced at the pilot scale,

Reference 21

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This paper cites Cohen, Statistical power analysis for the behavioral sciences.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Cohen, Statistical power analysis for the behavioral sciences

Reference 22

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This paper cites James, D.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks James, D

Reference 23

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Observation 56fcd7fa-ac70-432d-9f76-13764cfe839d · outbound

This paper cites A survey on image data augmentation for deep learning,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks A survey on image data augmentation for deep learning,

Reference 24

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Observation 817879bf-87c3-4f4f-a942-d050d50ff184 · outbound

This paper cites The effectiveness of data augmentation in image classification using deep learning,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks The effectiveness of data augmentation in image classification using deep learning,

Reference 25

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks An overview of gradient descent optimization algorithms

Reference 26

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Adam: A Method for Stochastic Optimization

Reference 27

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Decoupled Weight Decay Regularization

Reference 28

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Practical recommendations for gradient-based training of deep architectures,

Reference 29

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks SGDR: Stochastic Gradient Descent with Warm Restarts

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks,

Reference 31

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Pearson correlation coefficient,

Reference 32

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Early stopping-but when?,

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Multi-task learning using uncertainty to weigh losses for scene geometry and semantics,

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,

Reference 35

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Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 36

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This paper cites A computationally efficient hybrid neural network architecture for porous media: Integrating convolutional and graph neural networks for improved property predictions,.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks A computationally efficient hybrid neural network architecture for porous media: Integrating convolutional and graph neural networks for improved property predictions,

Reference 37

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This paper cites A Computationally Efficient Hybrid Neural Network Architecture for Porous Media: Integrating Convolutional and Graph Neural Networks for Improved Property Predictions.

Predicting Mechanical Properties of Lignin-Containing Polyurethane Rigid Foams from Microstructure Using Convolutional Neural Networks A Computationally Efficient Hybrid Neural Network Architecture for Porous Media: Integrating Convolutional and Graph Neural Networks for Improved Property Predictions

Reference 38

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