Paper Number
MP1                         My Program 

Session
Metzner Presentation

Title
Rheoinformatics: Seamlessly integrating theory, computation, and experiment through data-driven rheology

Presentation Date and Time
October 23, 2025 (Thursday) 8:00

Track / Room
Metzner Presentation / Sweeney Ballroom E+F

Authors (Click on name to view author profile)

1. Jamali, Safa (Northeastern University, Mechanical and Industrial Engineering)

Author and Affiliation Lines (in printed abstract book)
Safa Jamali
Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115

Speaker / Presenter 
Jamali, Safa

Keywords
theoretical methods; computational methods; artificial intelligence; methods; machine learning; networks; techniques

Text of Abstract
The scientific community has witnessed a plethora of developments in the area of machine learning and artificial intelligence in the last few years. Many scientific and engineering disciplines have adopted these advanced techniques under the general umbrella of scientific machine learning. Rheology community is also rapidly adopting a series of different data-driven techniques. I will present some of the recent advances in data-driven rheology, as a seamless pathway to bring theory, computation, and experiments together. These techniques will range from physics-informed machine learning platforms for meta-modeling complex fluids, to more advanced generative AI platforms for robust and reliable non-Newtonian fluid dynamics. Finally, I will discuss some of the future directions and outlooks, given the pace of developments in the ML/AI sciences.