3 citations · 3 across the 3 of their papers we have counts for
3 papers · 1 filter
PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks
Nahil Sobh, Rini Jasmine Gladstone, Hadi Meidani
Physics-Informed Neural Networks (PINNs) solve partial differential equations (PDEs) by embedding governing equations and boundary/initial conditions into the loss function. Howeve…
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…
Prediction and optimization of mechanical properties of composites using convolutional neural networks
Diab W. Abueidda, Mohammad Almasri, Rami Ammourah +3
In this paper, we develop a convolutional neural network model to predict the mechanical properties of a two-dimensional checkerboard composite quantitatively. The checkerboard com…