A Bayesian multiscale deep learning framework for flows in random media
type: Item
instance of: scholarly article
title: A Bayesian multiscale deep learning framework for flows in random media
author: Govinda Anantha Padmanabha
author: Nicholas Zabaras
published in: Foundations of Data Science
publication date: 2022-01-26T00:00:00Z
full work available at URL: https://arxiv.org/abs/2103.09056
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 68T37
zbMATH DE Number: 7464707
zbMATH Keywords: deep learning
zbMATH Keywords: neural networks
zbMATH Keywords: Bayesian
zbMATH Keywords: uncertainty quantification
zbMATH Keywords: multiscale
zbMATH Keywords: high-dimensionality
describes a project that uses: ImageNet
describes a project that uses: U-Net
describes a project that uses: AlexNet
describes a project that uses: FEniCS
describes a project that uses: PyTorch
describes a project that uses: Adam
MaRDI profile type: Publication
OpenAlex ID: W3174635800
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DOI: 10.3934/FODS.2021016
description: scientific article; zbMATH DE number 7464707