Paper Citation Record · LEDGER

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2504.14782.

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

pith.paper-citation-record.v1
2504.14782 v1

Coverage vector

measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 115ed68d-3b61-4bfa-9749-32defaad41b5 · outbound

This paper cites Clemens, S.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Clemens, S

Reference 1

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Observation 87292b65-259d-48d2-9fa4-26a10ceed7b3 · outbound

This paper cites Review—Modeling Methods for Analysis of Electromigration Degradation in Nano-Interconnects,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Review—Modeling Methods for Analysis of Electromigration Degradation in Nano-Interconnects,

Reference 2

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Observation 17e94967-c78a-4992-9579-b40323fce09c · outbound

This paper cites Effect of metal line width on electromigration of BEOL Cu interconnects,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Effect of metal line width on electromigration of BEOL Cu interconnects,

Reference 3

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This paper cites Microstructure Evolution and Effect on Resistivity for Cu Nanointerconnects and Beyond,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Microstructure Evolution and Effect on Resistivity for Cu Nanointerconnects and Beyond,

Reference 4

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Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Unresolved cited work

Reference 5

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Observation 214e2e23-6ac1-4a87-a4b1-2ec4362b1164 · outbound

This paper cites an unresolved cited work.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Unresolved cited work

Reference 6

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Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Unresolved cited work

Reference 7

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This paper cites Machine vision for three - dimensional scenes.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Machine vision for three - dimensional scenes

Reference 8

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This paper cites A Computational Approach to Edge Detection,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model A Computational Approach to Edge Detection,

Reference 9

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Observation 1a3cb5e8-f156-45b3-922d-905dd0160d46 · outbound

This paper cites Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform,

Reference 10

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Observation a7866a64-df35-4b71-8f72-cc0a2fdb499a · outbound

This paper cites An automated methodology for grain segmentation and grain size measurement from optical micrographs,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model An automated methodology for grain segmentation and grain size measurement from optical micrographs,

Reference 11

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Observation 337c45c7-7ac0-4bd9-aa73-03d7fd578b45 · outbound

This paper cites De-noising Filters for TEM (Transmission Electron Microscopy) Image of Nanomaterials,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model De-noising Filters for TEM (Transmission Electron Microscopy) Image of Nanomaterials,

Reference 12

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Observation 4d744e89-0ae2-487f-8669-74aadb9abcc8 · outbound

This paper cites New methods for automatic quantification of microstructural features using digital image processing,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model New methods for automatic quantification of microstructural features using digital image processing,

Reference 13

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Observation 30d2b32e-1f5f-4397-9a46-4110d7320547 · outbound

This paper cites Deep learning object detection in materials science: Current state and future directions,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Deep learning object detection in materials science: Current state and future directions,

Reference 14

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Observation fa6b6ada-3470-4129-8ac0-143e57bc1985 · outbound

This paper cites Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Automated analysis of grain morphology in TEM images using convolutional neural network with CHAC algorithm,

Reference 15

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Observation c38f5a0d-8082-4c54-8db5-065572aa33dc · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Deep unsupervised learning using nonequilibrium thermodynamics,

Reference 16

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Observation 39434fb8-78de-4a8c-83b7-04c7df85df91 · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Denoising Diffusion Probabilistic Models

Reference 17

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This paper cites SDXK: improving latent diffusion models for high-resolution image synthesis,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model SDXK: improving latent diffusion models for high-resolution image synthesis,

Reference 18

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Observation 7e5ec794-0768-43ce-adef-6337c9d57c38 · outbound

This paper cites DiffWave: A Versatile Diffusion Model for Audio Synthesis,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model DiffWave: A Versatile Diffusion Model for Audio Synthesis,

Reference 19

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Observation 10c0153b-cc73-4c80-833f-457fdb8d3fdb · outbound

This paper cites Predicting sample size required for classification performance,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Predicting sample size required for classification performance,

Reference 20

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This paper cites Chakravorti, Electric Field Analysis, CRC Press, 2017.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Chakravorti, Electric Field Analysis, CRC Press, 2017

Reference 21

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Observation d8ffcf1f-c8a5-4212-aa81-4cb1e504a416 · outbound

This paper cites An electrostatic study of curvature effects on electric field stress in high voltage differentials,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model An electrostatic study of curvature effects on electric field stress in high voltage differentials,

Reference 22

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Observation a7132752-870a-4cfc-b63a-16774f1edb4f · outbound

This paper cites Kleppner and R.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Kleppner and R

Reference 23

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Observation e31966af-d36e-4fac-9ef8-070e5edd973d · outbound

This paper cites Computer Simulation of Powder Compaction of Spherical Particles,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Computer Simulation of Powder Compaction of Spherical Particles,

Reference 24

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Observation cb7e8708-20b5-4a1d-9b89-dd42b1f2eeb2 · outbound

This paper cites Simulation of polycrystalline structure with Voronoi diagram in Laguerre geometry based on random closed packing of spheres,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Simulation of polycrystalline structure with Voronoi diagram in Laguerre geometry based on random closed packing of spheres,

Reference 25

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Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Unresolved cited work

Reference 26

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Observation b5e6e863-5aad-4418-885a-01b02b805d86 · outbound

This paper cites An Experimental Analysis on the Sensitivity of the Most Widely Used Edge Detection Methods to Different Noise Types,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model An Experimental Analysis on the Sensitivity of the Most Widely Used Edge Detection Methods to Different Noise Types,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:03.020082Z

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Observation e9e993fa-b9fa-4cbd-b4e5-6ff363687100 · outbound

This paper cites A Review of Classic Edge Detectors,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model A Review of Classic Edge Detectors,

Reference 28

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Observation 21ad7ace-07ad-4763-81be-f6aec69f46b5 · outbound

This paper cites Numerical evaluation of grain boundary electron scattering in molybdenum thin films: A critical analysis for advanced interconnects,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Numerical evaluation of grain boundary electron scattering in molybdenum thin films: A critical analysis for advanced interconnects,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:02.967798Z

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Observation ca6dc13b-a31c-427c-9e47-01602328c53a · outbound

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Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Consistency Models,

Reference 30

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Observation 32feeba2-5841-45d4-9f48-9b1fac1a49e5 · outbound

This paper cites Generative Modeling by Estimating Gradients of the Data Distribution,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Generative Modeling by Estimating Gradients of the Data Distribution,

Reference 31

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Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Score-Based Generative Modeling through Stochastic Differential Equations,

Reference 32

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Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Two‐Dimensional Motion of Idealized Grain Boundaries,

Reference 33

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Observation f1259c66-7905-414f-b1c1-a3c3cbdc7223 · outbound

This paper cites Grain Shapes and Other Metallurgical Applications of Topology,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Grain Shapes and Other Metallurgical Applications of Topology,

Reference 34

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Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Unresolved cited work

Reference 35

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Observation 6f859836-6d70-4bea-9b17-0735ddbe99e0 · outbound

This paper cites A strategy for synthetic microstructure generation and crystal plasticity parameter calibration of fine-grain-structured dual-phase steel,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model A strategy for synthetic microstructure generation and crystal plasticity parameter calibration of fine-grain-structured dual-phase steel,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:02.789479Z

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Observation 8cc3ffd9-6c9d-4df0-8ea8-e55cf09369e2 · outbound

This paper cites A novel point inclusion test for convex polygons based on Voronoi tessellations,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model A novel point inclusion test for convex polygons based on Voronoi tessellations,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:02.763736Z

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This paper cites Adam: A Method for Stochastic Optimization.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Adam: A Method for Stochastic Optimization

Reference 38

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This paper cites Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction

Reference 39

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This paper cites An Overview of Overfitting and its Solutions,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model An Overview of Overfitting and its Solutions,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:02.741054Z

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Observation 7ec73c7c-dbef-4463-8c61-d2eba79dfe4b · outbound

This paper cites Attention Is All You Need,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Attention Is All You Need,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:02.717130Z

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Observation 968b087e-161d-4bc8-b736-a84a5fa3d927 · outbound

This paper cites Physics -informed machine learning,.

Novel Concept-Oriented Synthetic Data approach for Training Generative AI-Driven Crystal Grain Analysis Using Diffusion Model Physics -informed machine learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:02.689712Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

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