Generative models on phase space

📰 ArXiv cs.AI

Generative models can learn high-dimensional distributions in phase space, particularly useful for high-energy physics data

advanced Published 6 Apr 2026
Action Steps
  1. Identify high-dimensional distributions in phase space
  2. Apply deep generative models such as diffusion and flow matching to learn these distributions
  3. Use the learned models to sample from the distributions and generate new data
  4. Analyze the generated data to gain insights into the physical system
Who Needs to Know This

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

Key Insight

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

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

Key Takeaways

Generative models can learn high-dimensional distributions in phase space, particularly useful for high-energy physics data

Full Article

Title: Generative models on phase space

Abstract:
arXiv:2604.02415v1 Announce Type: cross Abstract: Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be concentrated on a submanifold of the data embedding space. For high-energy physics data, consisting of collections of relativistic energy-momentum 4-vectors, this submanifold can enforce extremely strong physically-moti
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