Jasiuk-Research-Group
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DEM_for_J2_plasticity
DEM_for_J2_plasticity PublicA deep energy method (DEM) to solve J2 elastoplasticity problems in 3D.
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S-DeepONet
S-DeepONet PublicA sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural network in the trunk. The branch network efficiently encodes t…
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ResUNet-DeepONet-Plasticity
ResUNet-DeepONet-Plasticity PublicImplementation of a ResUNet-based DeepONet for predicting stress distribution on variable input geometries subject to variable loads. A ResUNet is used in the trunk network to encode the variable i…
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LatticeOPT
LatticeOPT PublicA heuristics-based topology optimization algorithm for thin-walled lattice structures in Abaqus/Explicit
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DeepEnergy-TopOpt
DeepEnergy-TopOpt PublicDensity-based topology optimization via the deep energy method
Repositories
- Bio-inspired-low-porosity-structures-using-Neural-Networks-GRU-Implemenation Public
The GRU model, trained to predict stress-strain response and energy absorption, uses eight discrete parameters to characterize the design space. It efficiently predicts new design responses in 0.16 milliseconds, enabling the rapid performance evaluation of 128,000 designs any given strain rate and final strain.
Jasiuk-Research-Group/Bio-inspired-low-porosity-structures-using-Neural-Networks-GRU-Implemenation’s past year of commit activity - CMDS-14-impact-resistantance-NN-prediction- Public
Neural Networks to Explore Structure-Property Relations in Bio-Inspired Impact-Resistant Structures
Jasiuk-Research-Group/CMDS-14-impact-resistantance-NN-prediction-’s past year of commit activity - DeepONet-CrystalPlasticity Public
Material-Response-Informed DeepONet and its Application to Polycrystal Stress-strain Prediction in Crystal Plasticity
Jasiuk-Research-Group/DeepONet-CrystalPlasticity’s past year of commit activity - S-DeepONet-transient-predictions Public
An improved sequential DeepONet model implementation that uses a recurrent neural network (GRU) in the branch and a feed-forward neural network in the trunk. It can predict the full field solutions at multiple time steps given a time-dependent input function and the domain.
Jasiuk-Research-Group/S-DeepONet-transient-predictions’s past year of commit activity - S-DeepONet Public
A sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural network in the trunk. The branch network efficiently encodes time-dependent input functions, and the trunk network captures the spatial dependence of the full-field data.
Jasiuk-Research-Group/S-DeepONet’s past year of commit activity - ResUNet-DeepONet-Plasticity Public
Implementation of a ResUNet-based DeepONet for predicting stress distribution on variable input geometries subject to variable loads. A ResUNet is used in the trunk network to encode the variable input geometries, and a feed-forward neural network is used in the branch to encode the loading parameters.
Jasiuk-Research-Group/ResUNet-DeepONet-Plasticity’s past year of commit activity - DEM_for_J2_plasticity Public
A deep energy method (DEM) to solve J2 elastoplasticity problems in 3D.
Jasiuk-Research-Group/DEM_for_J2_plasticity’s past year of commit activity - LatticeOPT Public
A heuristics-based topology optimization algorithm for thin-walled lattice structures in Abaqus/Explicit
Jasiuk-Research-Group/LatticeOPT’s past year of commit activity