Material‐informed training of viscoelastic deep material networks
Abstract: Deep material networks (DMN) are a data‐driven homogenization approach that show great promise for accelerating concurrent two‐scale simulations. As a salient feature, DMNs are solely identified by linear elastic precomputations on representative volume elements. After parameter identification, DMNs act as surrogates for full‐field simulations of such volume elements with inelastic constituents. In this work, we investigate how the training on linear elastic data, i.e., how the choice of the loss function and the sampling of the training data, affects the accuracy of DMNs for inelastic constituents. We investigate linear viscoelasticity and derive a material‐informed sampling procedure for generating the training data and a loss function tailored to the problem at hand. These ideas improve the accuracy of an identified DMN and allow for significantly reducing the number of samples to be generated and labeled.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Material‐informed training of viscoelastic deep material networks ; volume:22 ; number:1 ; year:2023 ; extent:0
Proceedings in applied mathematics and mechanics ; 22, Heft 1 (2023) (gesamt 0)
- Creator
-
Gajek, Sebastian
Schneider, Matti
Böhlke, Thomas
- DOI
-
10.1002/pamm.202200143
- URN
-
urn:nbn:de:101:1-2023032514263696532765
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:00 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Gajek, Sebastian
- Schneider, Matti
- Böhlke, Thomas
Other Objects (12)
Autoregressive neural networks for predicting the behavior of viscoelastic materials
Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models
Physically informed artificial neural networks for atomistic modeling of materials
Deep learning informed multiphysics material modeling of fiber reinforced polymer nanocomposites
Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy
Efficient two‐scale simulations of microstructured materials using deep material networks
Bending wave based characterization of viscoelastic materials
Viscoelastic behavior of rubbery materials (C. M. Roland)
Dynamic Crack Growth in Viscoelastic Materials with Memory
Bridging auditory perception and natural language processing with semantically informed deep neural networks
Zone Method for Representing Relaxation Characteristics of Viscoelastic Materials
Master curves construction for viscoelastic functions of bituminous materials
Autoregressive neural networks for predicting the behavior of viscoelastic materials
Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models
Physically informed artificial neural networks for atomistic modeling of materials
Deep learning informed multiphysics material modeling of fiber reinforced polymer nanocomposites
Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy
Efficient two‐scale simulations of microstructured materials using deep material networks
Bending wave based characterization of viscoelastic materials
Viscoelastic behavior of rubbery materials (C. M. Roland)
Dynamic Crack Growth in Viscoelastic Materials with Memory
Bridging auditory perception and natural language processing with semantically informed deep neural networks
Zone Method for Representing Relaxation Characteristics of Viscoelastic Materials
Master curves construction for viscoelastic functions of bituminous materials
Autoregressive neural networks for predicting the behavior of viscoelastic materials
Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models
Physically informed artificial neural networks for atomistic modeling of materials
Deep learning informed multiphysics material modeling of fiber reinforced polymer nanocomposites
Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy
Efficient two‐scale simulations of microstructured materials using deep material networks
Bending wave based characterization of viscoelastic materials
Viscoelastic behavior of rubbery materials (C. M. Roland)
Dynamic Crack Growth in Viscoelastic Materials with Memory
Bridging auditory perception and natural language processing with semantically informed deep neural networks
Zone Method for Representing Relaxation Characteristics of Viscoelastic Materials