3 citations · 3 across the 3 of their papers we have counts for
7 papers
Deep learning-aided inverse design of porous metamaterials
Phu Thien Nguyen, Yousef Heider, Dennis M. Kochmann +1
The ultimate aim of the study is to explore the inverse design of porous metamaterials using a deep learning-based generative framework. Specifically, we develop a property-variati…
Physics-based Machine Learning for Computational Fracture Mechanics
Fadi Aldakheel, Elsayed S. Elsayed, Yousef Heider +1
This study introduces a physics-based machine learning framework for modeling both brittle and ductile fractures. Unlike physics-informed neural networks, which solve partial diffe…
CNN-powered micro- to macro-scale flow modeling in deformable porous media
Yousef Heider, Fadi Aldakheel, Wolfgang Ehlers
This work introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of micro-CT images of real mic…
Bayesian inversion for unified ductile phase-field fracture
Nima Noii, Amirreza Khodadadian, Jacinto Ulloa +4
The prediction of crack initiation and propagation in ductile failure processes are challenging tasks for the design and fabrication of metallic materials and structures on a large…
Multilevel Global-Local techniques for adaptive ductile phase-field fracture
Fadi Aldakheel, Nima Noii, Thomas Wick +2
This paper outlines a rigorous variational-based multilevel Global-Local formulation for ductile fracture. Here, a phase-field formulation is used to resolve failure mechanisms by…
Virtual Element Formulation For Finite Strain Elastodynamics
M. Cihan, F. Aldakheel, B. Hudobivnik +1
This work provides an efficient virtual element scheme for the modeling of nonlinear elastodynamics undergoing large deformations. The virtual element method (VEM) has been applied…