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Found 5 papers, 1 papers with code
Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods
To test these models, we compare their predictions to a~test dataset, excluded from the training process, a~dataset lying beyond the covered kinematic region, and theoretical predictions obtained within the spectral function approach.
Bayesian Reasoning for Physics Informed Neural Networks
We have adopted the Bayesian neural network framework to obtain posterior densities from Laplace approximation.
Deep learning for diffusion in porous media
In the first task, we propose two types of CNN models: the C-Net and the encoder part of the U-Net.
Self-Normalized Density Map (SNDM) for Counting Microbiological Objects
The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail.
Predicting Porosity, Permeability, and Tortuosity of Porous Media from Images by Deep Learning
Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ($\varphi$), permeability $k$, and tortuosity ($T$).
