Volume 62, Issue 10

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October 2023

Data-driven methods in Rheology

Issue Editors:

Kyung Hyun Ahn,
Safa Jamali

9 articles in this issue

Data-driven methods in Rheology
Kyung Hyun Ahn
Safa Jamali
Editorial 03 October 2023 Pages: 473 - 475
Bayesian coarsening: rapid tuning of polymer model parameters
Hansani Weeratunge
Dominic Robe
Elnaz Hajizadeh
Original Contribution Open access 05 July 2023 Pages: 477 - 490
Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution
Sean Farrington
Soham Jariwala
Antony N. Beris
Original Contribution 27 June 2023 Pages: 491 - 506
Machine learning methods for particle stress development in suspension Poiseuille flows
Amanda A. Howard
Justin Dong
Panos Stinis
Original Contribution 02 December 2023 Pages: 507 - 534
Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks
Kyle R. Lennon
Joshua David John Rathinaraj
James W. Swan
Original Contribution Open access 11 September 2023 Pages: 535 - 556
Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models
Donya Dabiri
Milad Saadat
Safa Jamali
Original Contribution Open access 26 August 2023 Pages: 557 - 568
Data-driven constitutive model of complex fluids using recurrent neural networks
Howon Jin
Sangwoong Yoon
Kyung Hyun Ahn
Original Contribution 02 August 2023 Pages: 569 - 586
Scattering-Informed Microstructure Prediction during Lagrangian Evolution (SIMPLE)—a data-driven framework for modeling complex fluids in flow
Charles D. Young
Patrick T. Corona
Michael D. Graham
Original Contribution 09 September 2023 Pages: 587 - 604
Classification of battery slurry by flow signal processing via echo state network model
Seunghoon Kang
Howon Jin
Kyung Hyun Ahn
Original Contribution 03 July 2023 Pages: 605 - 615

Volume 62, Issue 10

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