15 citations · 20 across the 21 of their papers we have counts for
5 papers · 1 filter
Predictions of Transient Vector Solution Fields with Sequential Deep Operator Network
Junyan He, Shashank Kushwaha, Jaewan Park +3
The Deep Operator Network (DeepONet) structure has shown great potential in approximating complex solution operators with low generalization errors. Recently, a sequential DeepONet…
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…
I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN)
Diab W. Abueidda, Mostafa E. Mobasher
Most currently available methods for modeling multiphysics, including thermoelasticity, using machine learning approaches, are focused on solving complete multiphysics problems usi…