Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented]
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| Název: | Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented] |
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| Autoři: | Fetni, Seifallah, Delahaye, Jocelyn, Habraken, Anne |
| Přispěvatelé: | UEE - Urban and Environmental Engineering - ULiège |
| Zdroj: | Software Impacts, 17, 100563 (2023-09) |
| Informace o vydavateli: | Elsevier B.V., 2023. |
| Rok vydání: | 2023 |
| Témata: | Deep learning, Image generation and processing, LSTM, PCA, Python development, Software, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques |
| Popis: | The passage describes the development of a numerical framework in Python to create and process a large dataset for time-series prediction using Deep Learning algorithms. The dataset is generated by solving the Cahn–Hilliard equation for spinodal decomposition of a binary alloy and is labeled to train the algorithms. Prior to training, dimensionality reduction is performed using Auto-encoders and Principal Component Analysis. The framework identifies three distinct latent dimensions/spaces for the datasets. The primary dataset was generated by running up to 10,000 High-Fidelity Phase-Field simulations in parallel using High-Performance Computing (HPC). The framework is compatible with all major operating systems and has been thoroughly tested on Python 3.7 and later versions. |
| Druh dokumentu: | journal article http://purl.org/coar/resource_type/c_6501 article peer reviewed |
| Jazyk: | English |
| Relation: | https://api.elsevier.com/content/article/PII:S2665963823001008?httpAccept=text/xml; https://github.com/SoftwareImpacts/SIMPAC-2023-89; urn:issn:2665-9638 |
| DOI: | 10.1016/j.simpa.2023.100563 |
| Přístupová URL adresa: | https://orbi.uliege.be/handle/2268/307932 |
| Rights: | open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| Přístupové číslo: | edsorb.307932 |
| Databáze: | ORBi |
| Abstrakt: | The passage describes the development of a numerical framework in Python to create and process a large dataset for time-series prediction using Deep Learning algorithms. The dataset is generated by solving the Cahn–Hilliard equation for spinodal decomposition of a binary alloy and is labeled to train the algorithms. Prior to training, dimensionality reduction is performed using Auto-encoders and Principal Component Analysis. The framework identifies three distinct latent dimensions/spaces for the datasets. The primary dataset was generated by running up to 10,000 High-Fidelity Phase-Field simulations in parallel using High-Performance Computing (HPC). The framework is compatible with all major operating systems and has been thoroughly tested on Python 3.7 and later versions. |
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| DOI: | 10.1016/j.simpa.2023.100563 |