161 to 170 of 577 Results
Apr 18, 2024 -
Primary drainage experiments and fractal dimensions
TAR Archive - 5.0 GB -
MD5: 7b8cf6930230bfcf0b6fba72c563cd06
Capillary number for the invading phase equal to 10-4. Viscosity ratio between the invading and the defending phase equal to 10. |
Apr 18, 2024 -
Primary drainage experiments and fractal dimensions
TAR Archive - 2.3 GB -
MD5: 40b770e498dea4c40e1d7d2ee385528b
Capillary number for the invading phase equal to 10-4. Viscosity ratio between the invading and the defending phase equal to 1. |
Apr 18, 2024 -
Primary drainage experiments and fractal dimensions
TAR Archive - 1.5 GB -
MD5: 8a800605b22541c778d28fc95562ee2e
Capillary number for the invading phase equal to 10-5. Viscosity ratio between the invading and the defending phase equal to 0.2. |
Apr 18, 2024 -
Primary drainage experiments and fractal dimensions
TAR Archive - 6.4 GB -
MD5: 4b8b4022b5b8f70610d1c6158c77984b
Capillary number for the invading phase equal to 10-5. Viscosity ratio between the invading and the defending phase equal to 10. |
Apr 18, 2024 -
Primary drainage experiments and fractal dimensions
TAR Archive - 2.3 GB -
MD5: 6f605e1b5133b749f0d4923fceb93687
Capillary number for the invading phase equal to 10-5. Viscosity ratio between the invading and the defending phase equal to 1. |
Feb 16, 2024 - PN3-5
Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/DARUS-3881, DaRUS, V1
The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the concepts of machine learning (ML) to predict the onset of wrinkling insta... |
Jupyter Notebook - 13.7 KB -
MD5: dacad485cece6350acf08afa30461e19
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application/x-yaml - 8.5 KB -
MD5: 86f5d2848b4668165c8e936214efed7f
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Jupyter Notebook - 17.6 KB -
MD5: 060c1d1f671f506010836b1ef8333a41
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Jupyter Notebook - 12.2 KB -
MD5: e489576c86ed8d8a49cfd1ddce73b94e
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