Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets
<jats:p>Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design.</jats:p>
Main Author: | Buehler, Markus J |
---|---|
Other Authors: | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics |
Format: | Article |
Language: | English |
Published: |
Royal Society of Chemistry (RSC)
2022
|
Online Access: | https://hdl.handle.net/1721.1/146552 |
Similar Items
-
End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks
by: Buehler, Eric L, et al.
Published: (2023) -
Generating bright-field images of stained tissue slices from Mueller matrix polarimetric images with CycleGAN using unpaired dataset
by: Fan, J, et al.
Published: (2024) -
Rain removal using cycle-consistency adversarial network
by: Ng, Henry Siong Hock
Published: (2019) -
AttentionGAN: unpaired image-to-image translation using attention-guided generative adversarial networks.
by: Tang, H, et al.
Published: (2021) -
Observation of an unpaired photonic Dirac point
by: Liu, Gui-Geng, et al.
Published: (2020)