Collection

Rheologica Acta Presents Special Issue: Data-driven Methods in Rheology

Submission status
Closed
This special issue of Rheologica Acta presents a collection of early adoptions of machine learning to rheological sciences and serves as a foundation for further developments in this area. The papers published in this special issue sample a diverse set of data-driven approaches, from acceleration of polymer models, to constitutive model development and solution, and to high throughput characterization of complex fluids from experimental data.

Editors

  • Dr. Kyung Hyun Ahn Dr. Kyung Hyun Ahn  &

    Dr. Kyung Hyun Ahn

    Dr. Kyung Hyun Ahn received his Ph.D. degree at Seoul National University in 1991. He worked for Samsung Cheil Industries for six years before joining Seoul National University in 2001. He has initiated many new research programs at Seoul National University, all centered around introducing rheology and rheometric techniques to industry. He served as the president of the Korean Society of Rheology and is currently the director of the Center for Nano-structured Polymer Processing Technology, as well as the Center for Coating Materials and Processing.

  • Dr. Safa Jamali Dr. Safa Jamali

    Dr. Safa Jamali

    Dr. Safa Jamali is an Associate Professor of Mechanical and Industrial Engineering at Northeastern University, where he has been since 2017. His research group's activities are currently focused on developing and using a series of data-driven and computational techniques to study physics and rheology of complex fluids. Science-based data-driven methods and machine-learning platforms for rheological applications have been a major thrust of his efforts in recent years.

Articles

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