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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the...

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Bibliographic Details
Main Author: Fritzen, Felix
Other Authors: Ryckelynck, David
Format: eBook
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
Subjects:
Machine Learning
Sampling
Deim
Proper Symplectic Decomposition (psd)
Data Science
Dynamic Extrapolation
Nonlinear Reduced Order Model
Microstructure Property Linkage
Gnat
Computational Homogenization
Model Order Reduction (mor)
Model Order Reduction
Geometric Nonlinearity
Tensor-train Decomposition
Supervised Machine Learning
Hyper-reduction
Proper Orthogonal Decomposition
Surrogate Modeling
History Of Engineering & Technology / Bicssc
Reduced-order Model
Structure Preservation Of Symplecticity
Model Reduction
Empirical Cubature
Hencky Strain
Randomised Svd
Stabilization
Computational Homogenisation
Low-rank Approximation
Inverse Problem Plasticity
Nonlinear Material Behaviour
Reduced Integration Domain
Pod
Heterogeneous Data
Error Indicator
Symplectic Model Order Reduction
Gappy Pod
Empirical Cubature Method
Reduced Order Modeling (rom)
Nonlinear Structural Mechanics
Unsupervised Machine Learning
Pgd Compression
A Priori Enrichment
Reduced Basis
Snapshot Proper Orthogonal Decomposition
Proper Generalised Decomposition (pgd)
Parameter-dependent Model
Elastoviscoplastic Behavior
Proper Orthogonal Decomposition (pod)
Enhanced Pod
Archive
Modal Analysis
Hamiltonian System
Artificial Neural Networks
Elasto-viscoplasticity
Finite Deformation
Neural Network
Ecsw
Large Strain
3d Reconstruction
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
Physical Description:1 electronic resource (254 p.)
ISBN:9783039214105
books978-3-03921-410-5
9783039214099

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