Deep Convolutional Neural Networks for Eigenvalue Problems in Mechanics
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'This dataset repository contains sample codes and data used for the 2D results in the publication. The dataset (data2D_gzipped_famfiles_%d.h5) is a family of files under the HDF5 format that has been compressed under GZIP (compression factor = 8). All files must be within the same directory for readability. The compression for these files is transparent (no need to decompress, just straight reading). TF/Keras-based Python code is also attached under an open-source license. Further comments for the sample code are inside the files' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Convolutional Neural Networks, Deep Learning in Mechanics, Phoninic Crystal, Data-driven Eigenvalue Problem Solutions"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This dataset repository contains sample codes and data used for the 2D results in the publication. The dataset (data2D_gzipped_famfiles_%d.h5) is a family of files under the HDF5 format that has been compressed under GZIP (compression factor = 8). All files must be within the same directory for readability. The compression for these files is transparent (no need to decompress, just straight reading). TF/Keras-based Python code is also attached under an open-source license. Further comments for the sample code are inside the files' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This dataset repository contains sample codes and data used for the 2D results in the publication. The dataset (data2D_gzipped_famfiles_%d.h5) is a family of files under the HDF5 format that has been compressed under GZIP (compression factor = 8). All files must be within the same directory for readability. The compression for these files is transparent (no need to decompress, just straight reading). TF/Keras-based Python code is also attached under an open-source license. Further comments for the sample code are inside the files
Convolutional Neural Networks, Deep Learning in Mechanics, Phoninic Crystal, Data-driven Eigenvalue Problem Solutions