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

_{^{(Young et al., Scattering-Informed Microstructure Prediction during Lagrangian Evolution (SIMPLE)—a data-driven framework for modeling complex fluids in flow. Rheol Acta (2023).)}}

About the Issue 

Machine learning and data-driven methods have emerged as new tools enabling a paradigm shift in fundamental and applied rheological sciences alike. From the parametrization of constitutive models to the acceleration of computational efforts and practical material classifications, data-driven techniques impact rheology research. Rheology has a strong and robust history of incorporating computational/numerical tools and can benefit immensely by leveraging data-driven methodologies as well. 

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. 

 

Guest Editors

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 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.

 

Available October 2023