[Soft-matter] Prof. Alidad Amirfazli - York U Canada - Auditorium IP PAS - April 8 - 11:00 am

[Panagiotis Theodorakis]

Dear all,

Prof. Alidad Amirfazli (York University, Canada) will visit the 
Institute of Physics PAS and will give a seminar (details below) in the 
Leonard Sosnowski Auditorium IPPAS and on Zoom on April 8 at 11:00 am.

You are all welcome to attend this event.

Kind regards,

Panos

-----Announcement -------

You are cordially invited to

_The End of the Road for Empirical Correlations for Drop Impact Studies: 
A Machine Learning Approach_

_given by_

Prof.  Alidad Amirfazli

_Department of Mechanical Engineering, York University, Toronto, Canada_

on April 8, 2026, at 11:00 am CET in the Leonard Sosnowski Auditorium, 
Al. Lotników 32/46, 02-668 Warszawa at IP PAS
and on Zoom: 
https://zoom.us/j/91037790833?pwd=3vxbYujtlEAe5dJUx5KNisIIXRBbaJ.1   
(Meeting id: 910 3779 0833, code: 127945)

Duration: 45 min + question time

Abstract of the talk:

There are many different empirical correlations in the literature to 
describe the maximum spreading of droplets upon their impact onto a 
surface. The reason for this has been various parameter ranges or impact 
conditions past studies have used to establish each of the correlations 
as per We and Re numbers, or wettability of the substrate. I will 
present our latest work on droplet impact onto surfaces where we have 
used a novel approach to predict the maximum spreading of a droplet in a 
unified matter over a wide range of Weber, Reynolds numbers, and 
wettability as characterized by contact angle. Regression models using 
machine learning demonstrate high accuracy in predicting the maximum 
spreading, providing valuable insights into the underlying dynamics of 
droplet impacts using dependency parameters (i.e., SHAP analysis). This 
approach surpasses traditional methods reliant on empirical 
correlations, offering robust generalization to unseen scenarios, both 
within and beyond the range of training data, and demonstrating 
significant transformative potential to advance the field of droplet 
impact studies. Furthermore, machine learning can be used to effectively 
generate detailed regime maps also for classification of impact outcomes 
as will be discussed for compound droplet impact.

About the Speaker:

Prof. Alidad Amirfazli is the founding Chair of the Department of 
Mechanical Engineering at York University, Toronto, Canada, where he is 
currently a Professor. His research interests include surface 
engineering, heat transfer, and fluid mechanics, particularly focusing 
on droplet surface interactions, and recently integration of AI in 
research. Prof. Amirfazli has contributed significantly to his field 
with numerous publications and patents, and he has been recognized with 
several awards and honors for his work, e.g. King Charles III Coronation 
Medal, Annual Killam Professorship, appointment to the College of New 
Scholars, Royal Society of Canada, and being a Fellow of Engineering 
Institute of Canada. He has also been involved in extensive 
collaboration with both industry and academic partners.
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