Published March 30, 2021
| Version v.0.0
Software
Open
Data for BubbleNet code & micro-bubbles system dynamics simulation
Description
This file contains the dataset and the initial version of code used for BubbleNet. BubbleNet is a novel deep learning frame work building on top of Physics-Informed Neural Networks used for predicting micro-bubble system dynamics widely applied in bioengineering, chemical engineering, medical engineering, etc. The file includes the simulation program, the data for deep learning training, the original code for running the program & the code for generating the figure.
Files
Files
(42.6 GB)
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md5:661fdf4d35090ecdcb274c2a7b9af3c4
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159.5 MB | Download |
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md5:ba9d9fd0e4ce8477af1bd148b19e9db5
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18.0 kB | Download |
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md5:8130814a29727f11c44f61dd4395eacb
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28.9 GB | Download |
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md5:fde524ed336c84f686feb16133711fb9
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10.2 kB | Download |
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md5:0b1f3298fccd896ea5f3ef57b2ca6d0a
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13.3 kB | Download |
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md5:56b8502efbb1ec308238b4824cdbec1e
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11.8 kB | Download |
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md5:e3176815799b8dee34a77dd5e7ea1a2b
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16.6 kB | Download |
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md5:e0a6e22c7fd0c9dfa44dac6f2b063beb
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19.8 kB | Download |
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md5:d646512116ec517f4193c2cdbf4c4621
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22.7 MB | Download |
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md5:48b0476a3319a46be97d5a8e7eff7a22
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18.1 kB | Download |
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md5:a851d204e2f31753129e02a8ceb0e2b3
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16.6 kB | Download |
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md5:a0d848be4c8672e5f8df2bcbba6d5c4a
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667.4 MB | Download |
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md5:3713b3e1aa825581abab793393a1c052
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959 Bytes | Download |
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md5:c6e2d01527c8fad0111c89247e8b9bda
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2.0 kB | Download |
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md5:b43ab1ee7252d17eca8557455ca2dd8c
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434.6 MB | Download |
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md5:7df4d13f5a2ec62baaa86da8c6723129
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1.5 GB | Download |
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md5:ae6e7a3309a43d70d89648b8a7efa706
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1.7 GB | Download |
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md5:b0d1aa2d1ed5e939e6ef29d04b4b3984
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1.7 GB | Download |
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md5:44aa033cb699495096701f5d0725fee5
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134 Bytes | Download |
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md5:77ea7efe29b0f3ea8575b9847aa23847
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149 Bytes | Download |
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md5:f1c5704e59f8d186387c9d034b5b58a8
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784.1 MB | Download |
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md5:d1e8bfc23b5acda058a8dc9bd61b6c8a
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7.5 MB | Download |
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md5:2d178a5931b5f0320509a22c04b029c6
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8.1 MB | Download |
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md5:c8e00add9dadc360378f765ab1109beb
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8.0 MB | Download |
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md5:ba53bca366c2bc6ac15dc2188313b991
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5.3 kB | Download |
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md5:28e9da45195c1ce3514375b0efd8fdd1
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1.6 GB | Download |
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md5:fbc752efc56c76636a1eed65ed0705b0
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1.6 GB | Download |
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md5:47907376d415203b591f06c172a41a78
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1.7 GB | Download |
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md5:67557c6d1998bef2e21d49139fc7e27a
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1.7 GB | Download |
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md5:e91a90c874bd68a65030a9fa38b379c4
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30.4 MB | Download |
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md5:c531c1a33261336b0e19ef18dec02b09
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639.0 kB | Download |
Additional details
Related works
- Is cited by
- Preprint: arXiv:2105.07179 (arXiv)
References
- Zhai, H and Hu, G. (2021) Code associated to arXiv:2105.07179