Deep learning for model order reduction of multibody systems to minimal coordinates
type: Item
instance of: scholarly article
title: Deep learning for model order reduction of multibody systems to minimal coordinates
zbMATH Open document ID: 1506.70010
author: Andrea Angeli
author: F. Naets
author: Wim Desmet
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2021-04-26T00:00:00Z
Mathematics Subject Classification ID: 70E55
Mathematics Subject Classification ID: 70-08
zbMATH DE Number: 7337797
zbMATH Keywords: multibody dynamics
zbMATH Keywords: minimal coordinates
zbMATH Keywords: deep learning
zbMATH Keywords: model order reduction
describes a project that uses: TensorFlow
describes a project that uses: darch
describes a project that uses: Adam
describes a project that uses: DiffSharp
MaRDI profile type: Publication
full work available at URL: j.cma.2020.113517
OpenAlex ID: W3096985131
cites work: Q4338697
cites work: Q4500775
cites work: Real-Time Multibody Dynamics and Applications
cites work: A model reduction method for the control of rigid mechanisms
cites work: Online state and input force estimation for multibody models employing extended Kalman filtering
cites work: Learning Deep Architectures for AI
cites work: Q5270493
cites work: A predictor algorithm for fast geometrically-nonlinear dynamic analysis.
cites work: Computational mechanics enhanced by deep learning
cites work: Deep learning acceleration of total Lagrangian explicit dynamics for soft tissue mechanics
cites work: The global modal parameterization for non-linear model-order reduction in flexible multibody dynamics
cites work: Force coupling versus differential algebraic description of constrained multibody systems
cites work: Real-time flexible multibody simulation with global modal parameterization
cites work: Reducing the Dimensionality of Data with Neural Networks
cites work: Linearly Recurrent Autoencoder Networks for Learning Dynamics
cites work: A Machine Learning Approach for Minimal Coordinate Multibody Simulation
cites work: Q4558473
cites work: Principal component analysis.
DOI: 10.1016/J.CMA.2020.113517
description: scientific article; zbMATH DE number 7337797