75 citations · 156 across the 16 of their papers we have counts for
5 papers · 1 filter
Image-based adaptive domain decomposition for continuum damage models
Panos Pantidis, Cornelius Otchere, Mostafa E. Mobasher
We present a novel image-based adaptive domain decomposition FEM framework to accelerate the solution of continuum damage mechanics problems. The key idea is to use image-processin…
Damage identification for bridges using machine learning: Development and application to KW51 bridge
Yuqing Qiu, Bilal Ahmed, Diab W. Abueidda +6
The available tools for damage identification in civil engineering structures are known to be computationally expensive and data-demanding. This paper proposes a comprehensive mach…
Physics-informed DeepONet with stiffness-based loss functions for structural response prediction
Bilal Ahmed, Yuqing Qiu, Diab W. Abueidda +4
Finite element modeling is a well-established tool for structural analysis, yet modeling complex structures often requires extensive pre-processing, significant analysis effort, an…
DeepOKAN: Deep Operator Network Based on Kolmogorov Arnold Networks for Mechanics Problems
Diab W. Abueidda, Panos Pantidis, Mostafa E. Mobasher
The modern digital engineering design often requires costly repeated simulations for different scenarios. The prediction capability of neural networks (NNs) makes them suitable sur…
I-FENN with Temporal Convolutional Networks: expediting the load-history analysis of non-local gradient damage propagation
Panos Pantidis, Habiba Eldababy, Diab Abueidda +1
In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors, can efficiently simulate t…