Oral Infection - Parkinson's Disease, Concepts of Neuroinflammation. Could there be a New Investigation Model?

Mishra, Ashank; Jens, Schwamborn; De, Wever Bart; Mascolo, Andrea

doi:10.36959/704/677

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Such approaches lack the generalization capability of deep learning and often require prior knowledge of material characteristics or probability distributions of the target materials. Supervised deep learning predictive [24] and generative [25] models have emerged in recent years with applications in material science. Supervised learning models are inherently limited by the loss function, upon which they rely to evaluate performance.…”

Section: Introductionmentioning

confidence: 99%

FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials

Argilaga

2023

Materials

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X-ray μCT imaging is a common technique that is used to gain access to the full-field characterization of materials. Nevertheless, the process can be expensive and time-consuming, thus limiting image availability. A number of existing generative models can assist in mitigating this limitation, but they often lack a sound physical basis. This work presents a physics-supervised generative adversarial networks (GANs) model and applies it to the generation of X-ray μCT images. FEM simulations provide physical information in the form of elastic coefficients. Negative X-ray μCT images of a Hostun sand were used as the target material. During training, image batches were evaluated with nonparametric statistics to provide posterior metrics. A variety of loss functions and FEM evaluation frequencies were tested in a parametric study. The results show, that in several test scenarios, FEM-GANs-generated images proved to be better than the reference images for most of the elasticity coefficients. Although the model failed at perfectly reproducing the three out-of-axis coefficients in most cases, the model showed a net improvement with respect to the GANs reference. The generated images can be used in data augmentation, the calibration of image analysis tools, filling incomplete X-ray μCT images, and generating microscale variability in multiscale applications.

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Section: Introductionmentioning

confidence: 99%

FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials

Argilaga

2023

Materials

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“…The results showed that the use of five layers instead of three layers improved the accuracy of the CNN model. The elastic properties of composite materials, E 11 , E 22 and G 12 , were predicted using a CNN model [38]. In order to create the dataset, the values corresponding to these properties were generated using an FEM.…”

Section: Introductionmentioning

confidence: 99%

A New Method to Predict Damage to Composite Structures Using Convolutional Neural Networks

Mezeix,

Rivas,

Relandeau

et al. 2023

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To reduce the cost of developing composite aeronautical structures, manufacturers and university researchers are increasingly using “virtual testing” methods. Then, finite element methods (FEMs) are intensively used to calculate mechanical behavior and to predict the damage to fiber-reinforced polymer (FRP) composites under impact loading, which is a crucial design aspect for aeronautical composite structures. But these FEMs require a lot of knowledge and a significant number of IT resources to run. Therefore, artificial intelligence could be an interesting way of sizing composites in terms of impact damage tolerance. In this research, the authors propose a methodology and deep learning-based approach to predict impact damage to composites. The data are both collected from the literature and created using an impact simulation performed using an FEM. The data augmentation method is also proposed to increase the data number from 149 to 2725. Firstly, a CNN model is built and optimized, and secondly, an aggregation of two CNN architectures is proposed. The results show that the use of an aggregation of two CNNs provides better performance than a single CNN. Finally, the aggregated CNN model prediction demonstrates the potential for CNN models to accelerate composite design by showing a 0.15 mm precision for all the length measurements, an average delaminated surface error of 56 mm2, and an error rate of 7% for the prediction of the presence of delamination.

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Oral Infection - Parkinson's Disease, Concepts of Neuroinflammation. Could there be a New Investigation Model?

Cited by 2 publications

References 9 publications

FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials

FEM-GAN: A Physics-Supervised Deep Learning Generative Model for Elastic Porous Materials

A New Method to Predict Damage to Composite Structures Using Convolutional Neural Networks

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