GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data | Paper Citation Record

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This paper cites Optimization of fused filament fabrication process parameters under uncertainty to maximize part geometry accuracy.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Optimization of fused filament fabrication process parameters under uncertainty to maximize part geometry accuracy

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This paper cites Haberman, and Carolyn C.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Haberman, and Carolyn C

Reference 2

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This paper cites Haberman.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Haberman

Reference 3

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This paper cites Level set based robust shape and topology optimization under random field uncertainties.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Level set based robust shape and topology optimization under random field uncertainties

Reference 4

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This paper cites A new level-set based approach to shape and topology optimization under geometric uncertainty.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data A new level-set based approach to shape and topology optimization under geometric uncertainty

Reference 5

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This paper cites Wang, David Sell, Thaibao Phan, and Jonathan A.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Wang, David Sell, Thaibao Phan, and Jonathan A

Reference 6

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This paper cites Uncertainty quantification of microstructure variability and mechanical behavior of additively manufactured lattice structures.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Uncertainty quantification of microstructure variability and mechanical behavior of additively manufactured lattice structures

Reference 7

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This paper cites GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

Reference 8

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This paper cites Generative adversarial nets.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Generative adversarial nets

Reference 9

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This paper cites Denoising diffusion probabilistic models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Denoising diffusion probabilistic models

Reference 10

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This paper cites Maximum likelihood training of score-based diffusion models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Maximum likelihood training of score-based diffusion models

Reference 11

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This paper cites Elucidating the design space of diffusion-based generative models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Elucidating the design space of diffusion-based generative models

Reference 12

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This paper cites Diffusion models beat gans on image synthesis.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Diffusion models beat gans on image synthesis

Reference 13

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Deep unsupervised learning using nonequilibrium thermodynamics

Reference 14

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This paper cites High-resolution image synthesis with latent diffusion models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data High-resolution image synthesis with latent diffusion models

Reference 15

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This paper cites Palette: Image-to-image diffusion models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Palette: Image-to-image diffusion models

Reference 16

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This paper cites Cnn features off-the-shelf: an astounding baseline for recognition.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Cnn features off-the-shelf: an astounding baseline for recognition

Reference 17

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This paper cites How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data How transferable are features in deep neural networks? Advances in neural information processing systems, 27, 2014

Reference 18

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This paper cites Transfer Learning for Diffusion Models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Transfer Learning for Diffusion Models

Reference 19

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This paper cites Fine-tuning diffusion models with limited data.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Fine-tuning diffusion models with limited data

Reference 20

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This paper cites Difffit: Unlocking transferability of large diffusion models via simple parameter-efficient fine-tuning.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Difffit: Unlocking transferability of large diffusion models via simple parameter-efficient fine-tuning

Reference 21

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This paper cites One-shot generative domain adaptation.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data One-shot generative domain adaptation

Reference 22

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This paper cites Few-shot Image Generation with Diffusion Models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Few-shot Image Generation with Diffusion Models

Reference 23

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This paper cites Generalized one-shot domain adaptation of generative adversarial networks.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Generalized one-shot domain adaptation of generative adversarial networks

Reference 24

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This paper cites A closer look at few-shot image generation.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data A closer look at few-shot image generation

Reference 25

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This paper cites Explicit inductive bias for transfer learning with convolutional networks.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Explicit inductive bias for transfer learning with convolutional networks

Reference 26

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This paper cites Provable sample-efficient transfer learning conditional diffusion models via representation learning.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Provable sample-efficient transfer learning conditional diffusion models via representation learning

Reference 27

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This paper cites Transfer learning for diffusion models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Transfer learning for diffusion models

Reference 28

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This paper cites Denoising diffusion probabilistic models for addressing data limitations in chest x-ray classification.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Denoising diffusion probabilistic models for addressing data limitations in chest x-ray classification

Reference 29

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This paper cites Semantic image synthesis with spatially-adaptive normalization.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Semantic image synthesis with spatially-adaptive normalization

Reference 30

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This paper cites Mechanical cloak via data- driven aperiodic metamaterial design.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Mechanical cloak via data- driven aperiodic metamaterial design

Reference 31

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This paper cites Freeu: Free lunch in diffusion u-net.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Freeu: Free lunch in diffusion u-net

Reference 32

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This paper cites Kingma and Jimmy Ba.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Kingma and Jimmy Ba

Reference 33

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This paper cites Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces

Reference 34

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This paper cites Statistical modeling of images with fields of gaussian scale mixtures.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Statistical modeling of images with fields of gaussian scale mixtures

Reference 35

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This paper cites Improved precision and recall metric for assessing generative models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Improved precision and recall metric for assessing generative models

Reference 36

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This paper cites Reliable fidelity and diversity metrics for generative models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Reliable fidelity and diversity metrics for generative models

Reference 37

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GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Design of materials using topology optimization and energy-based homogenization approach in matlab

Reference 38

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This paper cites Optimal transport: old and new, volume 338.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Optimal transport: old and new, volume 338

Reference 39

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This paper cites A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?

Reference 40

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GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data An Empirical Study of Scaling Laws for Transfer

Reference 41

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GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Scaling Laws for Transfer

Reference 42

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This paper cites Big transfer (bit): General visual representation learning.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Big transfer (bit): General visual representation learning

Reference 43

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This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Revisiting unreasonable effectiveness of data in deep learning era

Reference 44

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This paper cites What makes ImageNet good for transfer learning?.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data What makes ImageNet good for transfer learning?

Reference 45

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This paper cites Lora: Low-rank adaptation of large language models.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Lora: Low-rank adaptation of large language models

Reference 46

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This paper cites Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networks

Reference 47

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This paper cites One-shot generative domain adaptation.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data One-shot generative domain adaptation

Reference 49

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This paper cites Image synthesis under limited data: A survey and taxonomy.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data Image synthesis under limited data: A survey and taxonomy

Reference 50

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This paper cites as-fabricated.

GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data as-fabricated

Reference 51

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