Data-driven methods in Rheology
Author:
Publisher
Springer Science and Business Media LLC
Subject
Condensed Matter Physics,General Materials Science
Link
https://link.springer.com/content/pdf/10.1007/s00397-023-01416-w.pdf
Reference8 articles.
1. Dabiri D, Saadat M, Mangal D et al (2023) Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models. Rheol Acta. https://doi.org/10.1007/s00397-023-01408-w
2. Farrington S, Jariwala S, Armstrong M et al (2023) Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution. Rheol Acta. https://doi.org/10.1007/s00397-023-01402-2
3. Howard et al (2023) Machine learning methods for particle stress development in suspension Poiseuille flows. Rheol Acta. https://doi.org/10.1007/s00397-023-01402-2
4. Jin H, Yoon S, Park FC et al (2023) Data-driven constitutive model of complex fluids using recurrent neural networks. Rheol Acta. https://doi.org/10.1007/s00397-023-01405-z
5. Kang S, Jin H, Ahn CH et al (2023) Classification of battery slurry by flow signal processing via echo state network model. Rheol Acta. https://doi.org/10.1007/s00397-023-01404-0
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1. Short Review on Machine Learning-Based Multi-Scale Simulation in Rheology;Nihon Reoroji Gakkaishi;2024-02-15
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