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• W4302293995 endingPage "111820" @default.
• W4302293995 startingPage "111820" @default.
• W4302293995 abstract "In this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accurate low dimensionality reduction of simulated microstructural images and to provide time-series analysis. The dataset was indeed constructed from high-fidelity Phase-Field simulations. Analyses demonstrated that the association of auto-encoder neural networks and principal component analyses leads to ensure efficient and significant dimensionality reduction: 1/196 of reduction ratio with more than 80% of accuracy. These findings give insight to apply analyses on data from the latent dimension. Application of Long Short Term Memory (LSTM) neural networks showed the possibility of making next frame predictions; that makes possible the acceleration of Phase-Field simulation without the need of high computing resources. We discussed the application of such a framework on various areas of research. Different methods are proposed from the conducted analyses, in order to ensure dimensionality reduction (auto-encoders, principal component analysis, Artificial Neural Networks) and time-series analysis (LSTM, Gated Recurrent Unit (GRU)). • A novel and simple framework for accelerating Phase-Field simulations is proposed. • Auto-encoders ensure accurate low dimensionality representation of microstructures. • The combination PCAs - Auto-encoders ensures efficient dimensionality reduction. • LSTMs could accelerate Phase-Filed simulations if images are significantly reduced." @default.
• W4302293995 created "2022-10-06" @default.
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• W4302293995 date "2023-01-01" @default.
• W4302293995 modified "2023-10-16" @default.
• W4302293995 title "Capabilities of Auto-encoders and Principal Component Analysis of the reduction of microstructural images; Application on the acceleration of Phase-Field simulations" @default.
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• W4302293995 doi "https://doi.org/10.1016/j.commatsci.2022.111820" @default.
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