[en] Deep Material Network (DMN) is a homogenisation method that incorporates analytical homogenisation solutions and material constitutive relations into a neural network model yielding mechanistic building blocks [1]. DMN was reformulated from the interaction view point [2] in order to improve its training performance.
In this work, on the one hand, the phase volume fraction is decoupled from the topological parameters of the Interaction-Based DMN (IB-DMN). Since the phase volume fraction is no longer influenced by the topological parameters, a stochastic IB-DMN is constructed by introducing uncertainties to the topological parameters of a general IB-DMN trained with linear elastic homogenization data. The nonlinear predictions of the proposed stochastic IB-DMN are compared to those from Direct Numerical Simulations (DNSs) on 2D Stochastic Volume Elements (SVEs) of unidirectional fiber-reinforced matrix composites under a finite strain setting.
On the other hand, damage is introduced in the matrix constituent phase in order to test the performance of the IB-DMN upon softening.
Research Center/Unit :
A&M - Aérospatiale et Mécanique - ULiège
Disciplines :
Mechanical engineering
Author, co-author :
Wu, Ling ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Noels, Ludovic ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Computational & Multiscale Mechanics of Materials (CM3)
Language :
English
Title :
Stochastic Deep Material Networks as efficient surrogates for composites & Deep Material Networks performance for damaging processes
Publication date :
06 June 2025
Event name :
8th International Conference on Computational Modelling of Fracture and Failure of Materials and Structures (CFRAC 2025)
Event organizer :
ECCOMAS
Event place :
Porto, Portugal
Event date :
4-6 June 2025
Event number :
8th
Audience :
International
European Projects :
HE - 101056682 - DIDEAROT - Digital Design strategies to certify and mAnufacture Robust cOmposite sTructures
Name of the research project :
DIDEAROT
Funders :
EC - European Commission European Union
Funding number :
101056682
Funding text :
This project has received funding from the European Union’s Horizon Europe Framework Programme under grant agreement No. 101056682 for the project ‘‘DIgital DEsign strategies to certify and mAnufacture Robust cOmposite sTructures (DIDEAROT)’’. The contents of this publication are the sole responsibility of ULiege and do not necessarily reflect the opinion of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
