241 citations · 741 across the 9 of their papers we have counts for
6 papers · 1 filter
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…