@inproceedings{b84d2940e85d4dfba5166dddce0f2017,

title = "Model order reduction in viscoplastic flow modelling using proper orthogonal decomposition and neural networks",

abstract = "This document provides information and instructions for preparing a Full Paper to be included in the Proceedings of ECCM ECFD 2018 Conference. e present a method to construct reduced-order models for duct flows of Bingham media. Our method is based on proper orthogonal decomposition (POD) to find a low-dimensional approximation to the velocity and artificial neural network to approximate the coefficients of a given solution in the constructed POD basis. We use well-established augmented Lagrangian method and finite-element discretization in the “offline” stage. We show that the resulting approximation has a reasonable accuracy, but the evaluation of the approximate solution several orders of magnitude times faster.",

keywords = "Machine learning, Neural networks, Proper orthogonal decomposition, Viscoplastic flows",

author = "Muravleva, {Ekaterina A.} and Oseledets, {Ivan V.} and Koroteev, {Dmitry A.}",

year = "2020",

language = "English",

series = "Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018",

publisher = "International Centre for Numerical Methods in Engineering, CIMNE",

pages = "2475--2487",

editor = "Roger Owen and {de Borst}, Rene and Jason Reese and Chris Pearce",

booktitle = "Proceedings of the 6th European Conference on Computational Mechanics",

note = "6th ECCOMAS European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on Computational Fluid Dynamics, ECFD 2018 ; Conference date: 11-06-2018 Through 15-06-2018",

}