Section 1. Machine Learning in Fundamental Physics
Machine learning methods in particle astrophysics and high energy physics.
Fast event generators based on machine learning for simulation of physics phenomena.
Multi-messenger data analysis of experimental data.
Application machine learning for data analysis in megascience facilities.
Section 2. Machine Learning for Environmental Sciences
Climate analysis, retrospective analysis and projection
Statistical modeling of the ocean and atmosphere on various temporal and spatial scales
Environmental monitoring: remote sensing, instrumental monitoring, observations, monitoring networks
Section 3. Machine Learning in Natural Sciences
The conference will feature:
invited presentations – 30 minutes,
regular presentations – 15 minutes,