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pvcracks: trained VAE model

Dataset ·
DOI:https://doi.org/10.21948/2997860· OSTI ID:2997860
 [1];  [2];  [3];  [4];  [4];  [4]
  1. Sandia National Laboratories (SNL), Carlsbad, NM (United States). Waste Isolation Pilot Plant (WIPP) Site
  2. Case Western Reserve Univ., Cleveland, OH (United States)
  3. Univ. of Colorado, Boulder, CO (United States)
  4. Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
+ Show Author Affiliations

The resulting model weights for the variational autoencoder for solar cell crack parametrization to be loaded into the python code for other to use

Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Case Western Reserve Univ.; Univ. of Colorado, Boulder
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
DOE Contract Number:
AC36-08GO28308
OSTI ID:
2997860
Country of Publication:
United States
Language:
English

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Related Subjects

14 SOLAR ENERGY
deep learning
electroluminescence
model weights
multisolsegment
segmentation
solar cell defects