• Python Data Driven framework f...

Treffer: Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented]

Title:
Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented]
Authors:
Fetni, Seifallah, Delahaye, Jocelyn, Habraken, Anne
Contributors:
UEE - Urban and Environmental Engineering - ULiège
Source:
Software Impacts, 17, 100563 (2023-09)
Publisher Information:
Elsevier B.V., 2023.
Publication Year:
2023
Subject Terms:
Deep learning, Image generation and processing, LSTM, PCA, Python development, Software, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques
Document Type:
Fachzeitschrift journal article<br />http://purl.org/coar/resource_type/c_6501<br />article<br />peer reviewed
Language:
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
Access URL:
https://orbi.uliege.be/handle/2268/307932
Rights:
open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
Accession Number:
edsorb.307932
Database:
ORBi

Weitere Informationen

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.