224 citations · 1k across the 30 of their papers we have counts for
6 papers · 1 filter
A deep learning energy-based method for classical elastoplasticity
Junyan He, Diab Abueidda, Rashid Abu Al-Rub +2
The deep energy method (DEM) has been used to solve the elastic deformation of structures with linear elasticity, hyperelasticity, and strain-gradient elasticity material models ba…
On the use of graph neural networks and shape-function-based gradient computation in the deep energy method
Junyan He, Diab Abueidda, Seid Koric +1
A graph neural network (GCN) is employed in the deep energy method (DEM) model to solve the momentum balance equation in 3D for the deformation of linear elastic and hyperelastic m…
Deep energy method in topology optimization applications
Junyan He, Shashank Kushwaha, Charul Chadha +3
This paper explores the possibilities of applying physics-informed neural networks (PINNs) in topology optimization (TO) by introducing a fully self-supervised TO framework that is…
Enhanced physics-informed neural networks for hyperelasticity
Diab W. Abueidda, Seid Koric, Erman Guleryuz +1
Physics-informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, ph…
A deep learning energy method for hyperelasticity and viscoelasticity
Diab W. Abueidda, Seid Koric, Rashid Abu Al-Rub +3
The potential energy formulation and deep learning are merged to solve partial differential equations governing the deformation in hyperelastic and viscoelastic materials. The pres…
Surrogate Neural Network Model for Sensitivity Analysis and Uncertainty Quantification of the Mechanical Behavior in the Optical Lens-Barrel Assembly
Shantanu Shahane, Erman Guleryuz, Diab W Abueidda +7
Surrogate neural network-based models have been lately trained and used in a variety of science and engineering applications where the number of evaluations of a target function is…