Ausgabe 10/2023
Data-driven methods in Rheology
Inhalt (9 Artikel)
Open Access
Original Contribution
Bayesian coarsening: rapid tuning of polymer model parameters
Hansani Weeratunge, Dominic Robe, Adrian Menzel, Andrew W. Phillips, Michael Kirley, Kate Smith-Miles, Elnaz Hajizadeh
Original Contribution
Physiology-based parameterization of human blood steady shear rheology via machine learning: a hemostatistics contribution
Sean Farrington, Soham Jariwala, Matt Armstrong, Ethan Nigro, Norman J. Wagner, Antony N. Beris
Original Contribution
Machine learning methods for particle stress development in suspension Poiseuille flows
Amanda A. Howard, Justin Dong, Ravi Patel, Marta D’Elia, Martin R. Maxey, Panos Stinis
Open Access
Original Contribution
Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks
Kyle R. Lennon, Joshua David John Rathinaraj, Miguel A. Gonzalez Cadena, Ashok Santra, Gareth H. McKinley, James W. Swan
Open Access
Original Contribution
Fractional rheology-informed neural networks for data-driven identification of viscoelastic constitutive models
Donya Dabiri, Milad Saadat, Deepak Mangal, Safa Jamali
Original Contribution
Data-driven constitutive model of complex fluids using recurrent neural networks
Howon Jin, Sangwoong Yoon, Frank C. Park, Kyung Hyun Ahn
Original Contribution
Scattering-Informed Microstructure Prediction during Lagrangian Evolution (SIMPLE)—a data-driven framework for modeling complex fluids in flow
Charles D. Young, Patrick T. Corona, Anukta Datta, Matthew E. Helgeson, Michael D. Graham
Original Contribution
Classification of battery slurry by flow signal processing via echo state network model
Seunghoon Kang, Howon Jin, Chan Hyeok Ahn, Jaewook Nam, Kyung Hyun Ahn