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- inetnum : 47.251.0.0 - 47.251.255.255
- name : ALICLOUD-US
- handle : NET-47-251-0-0-1
- status : Reassigned
- created : 2018-07-24
- changed : 2018-07-24
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| Location | San Mateo United States |
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| Metro Code | 807 |
| Timezone | America/Los_Angeles |
| Postal | 94402 |
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Drdcn.coowor.comDA: 16 PA: 6 MOZ Rank: 22
GitHub - cics-nd/CAAE-DRDCN-inverse: Deep residual …
Update (2021-3-13): We have now uploaded the codes of 3D-DRDCN. For 2D problems, one can replace the 3D opterations (e.g., convolution and batchnorm) with their 2D counterparts in net design file (i.e. rrde_ed3D.py). Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian conductivities
Github.comDA: 10 PA: 27 MOZ Rank: 38
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Mymandir.comDA: 16 PA: 8 MOZ Rank: 26
CAAE-DRDCN-inverse/README at master · cics-nd/CAAE …
Deep residual networks for dimensionality reduction and surrogate modeling in high-dimensional inverse problems - CAAE-DRDCN-inverse/README at master · cics-nd/CAAE-DRDCN-inverse
Github.comDA: 10 PA: 50 MOZ Rank: 66
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on *:TEXT:*:*: { if ($nick == DRDCNR1 || $nick
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CAAE-DRDCN-inverse | Deep residual networks for dimensionality …
CAAE-DRDCN-inverse has a low active ecosystem. It has 1 star(s) with 4 fork(s). It had no major release in the last 12 months. It has a neutral sentiment in the developer community.
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Integration of Adversarial Autoencoders With Residual Dense ...
The DRDCN network's predictions for the output fields of 2-D and 3-D models given randomly selected input log-conductivity fields in the test sets are illustrated in Figures 6 and 7, respectively. For comparison, the predictions by the forward models and network approximation errors are also shown in each plot. It is observed that although the output fields are highly irregular with sharp ...
Agupubs.onlinelibrary.wiley.comDA: 31 PA: 25 MOZ Rank: 67
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[1906.11828] Integration of adversarial autoencoders with residual ...
The CCAE and DRDCN networks are incorporated into an iterative ensemble smoother to formulate an inversion framework. The numerical experiments performed using 2-D and 3-D solute transport models illustrate the performance of the integrated method. The obtained results indicate that the CAAE is a robust parameterization method for non-Gaussian ...
Arxiv.orgDA: 9 PA: 15 MOZ Rank: 37
Research Code
The CCAE and DRDCN networks are incorporated into an iterative ensemble smoother to formulate an inversion framework. The numerical experiments performed using 2-D and 3-D solute transport models illustrate the performance of the integrated method. The obtained results indicate that the CAAE is a robust parameterization method for non-Gaussian ...
Researchcode.comDA: 16 PA: 50 MOZ Rank: 36
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Numerade.comDA: 16 PA: 50 MOZ Rank: 32
Integration of adversarial autoencoders with residual dense ...
The CCAE and DRDCN networks are incorporated into an iterative ensemble smoother to formulate an inversion framework. The numerical experiments performed using 2-D and 3-D solute transport models illustrate the performance of the integrated method. The obtained results indicate that the CAAE is a robust parameterization method for non-Gaussian ...
Paperswithcode.comDA: 18 PA: 50 MOZ Rank: 88
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