Speaker
Dr
Vladyslav Naboka
(Bogolyubov Institute for Theoretical Physics)
Description
The integrated HydroKinetic Model (iHKM) is a key tool for simulating the complex dynamics of relativistic heavy-ion collisions. However, full-scale iHKM simulations are computationally demanding. This work presents a novel approach combining machine learning with iHKM to both infer optimal model parameters (such as viscosity and relaxation time) from experimental data and to approximate full simulation results with high speed and accuracy. The synergy of physics-based modeling and AI significantly accelerates the analysis pipeline, offering new possibilities for exploring collision energy regimes. The methodology and its validation on experimental datasets will be discussed.
Primary authors
Dr
Vladyslav Naboka
(Bogolyubov Institute for Theoretical Physics)
Musfer Adzhymambetov
(Bogolyubov Institute for Theoretical Physics)
