Conveners
Plenary Session 6: AI-Driven Advances in Fundamental Physics and Manufacturing Technology
- Doojin Kim (University of South Dakota)
This talk will review recent applications of quantum machine learning to problems in high energy particle physics motivated by the analysis of data from the Large Hadron Collider at CERN, Geneva. Typical tasks include the classifications of jets as quarks or gluons; the classification of calorimeter clusters as electrons or photons; generative modelling of fragmentation and hadronization in...
The IceCube Neutrino Observatory, located at the geographic South Pole, has opened a new window into the universe through the detection of high-energy astrophysical neutrinos. This contribution presents recent advances in the application of machine learning (ML) techniques to key challenges in astroparticle physics using IceCube data. We highlight the role of deep learning in improving event...
The extrusion dynamics in additive manufacturing (AM) processes
such as electrohydrodynamic (EHD) printing are dependent on a large (>15) set of
processing parameters, material properties, and environmental conditions.
In EHD printing, these parameters affect the process stability, printing
behavior (droplet or filament), and quality of the deposited microstructures. In
this work, we have...
