241 citations · 741 across the 9 of their papers we have counts for
9 papers
Designing impact-resistant bio-inspired low-porosity structures using neural networks
Shashank Kushwaha, Junyan He, Diab Abueidda +1
Biological structural designs in nature, like hoof walls, horns, and antlers, can be used as inspiration for generating structures with excellent mechanical properties. A common th…
Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads
Junyan He, Seid Koric, Shashank Kushwaha +3
A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as the trunk network is devised to predict full-field highly nonlinear elastic-plastic stress response for…
Sequential Deep Operator Networks (S-DeepONet) for Predicting Full-field Solutions Under Time-dependent Loads
Junyan He, Shashank Kushwaha, Jaewan Park +3
Deep Operator Network (DeepONet), a recently introduced deep learning operator network, approximates linear and nonlinear solution operators by taking parametric functions (infinit…
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…
LatticeOPT: A heuristic topology optimization framework for thin-walled, 2D extruded lattices
Junyan He, Shashank Kushwaha, Diab Abueidda +1
This paper introduces a heuristic topology optimization framework for thin-walled, 2D extruded lattice structures subject to complex high-speed loading. The proposed framework opti…
Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks
Junyan He, Shashank Kushwaha, Diab Abueidda +1
This paper investigates the structure-property relations of thin-walled lattices under dynamic longitudinal compression, characterized by their cross-sections and heights. These re…
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…