126 citations · 371 across the 12 of their papers we have counts for
6 papers · 1 filter
Advanced Deep Operator Networks to Predict Multiphysics Solution Fields in Materials Processing and Additive Manufacturing
Shashank Kushwaha, Jaewan Park, Seid Koric +3
Unlike classical artificial neural networks, which require retraining for each new set of parametric inputs, the Deep Operator Network (DeepONet), a lately introduced deep learning…
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…
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…