Generative models on phase space

1. 1Identify high-dimensional distributions in phase space
2. 2Apply deep generative models such as diffusion and flow matching to learn these distributions
3. 3Use the learned models to sample from the distributions and generate new data
4. 4Analyze the generated data to gain insights into the physical system

ML researchers and physicists can benefit from this research as it provides a new approach to modeling complex physical systems, allowing them to better understand and analyze high-energy physics data

💡 Deep generative models can effectively model complex physical systems by learning high-dimensional distributions in phase space

💡 Generative models can learn high-dimensional distributions in phase space!