mlsanityData to 7 Oct 2026

A survey of machine learning-based physics event generation

arXiv 2106.00643 ↗First posted 1 Jun 2021hep-ph cs.LG nucl-excited by 6

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Abstract (arXiv)

Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state-of-the-art of machine learning (ML) efforts at building physics event generators. We review ML generative models used in ML-based event generators and their specific challenges, and discuss various approaches of incorporating physics into the ML model designs to overcome these challenges. Finally, we explore some open questions related to super-resolution, fidelity, and extrapolation for physics event generation based on ML technology.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 64 references that are in the map. First posted on arXiv 1 Jun 2021.

Builds on

15 of 64 references.

References · 15

  1. 2013Auto-Encoding Variational Bayescited by 3,980
  2. 2016Improved Techniques for Training GANscited by 2,938
  3. 2017Improved Training of Wasserstein GANscited by 2,223
  4. 2014Conditional Generative Adversarial Netscited by 2,152
  5. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  6. 2016Variational Inference: A Review for Statisticianscited by 1,466
  7. 2016Least Squares Generative Adversarial Networkscited by 1,000
  8. 2017Wasserstein GANcited by 635
  9. 2017Wasserstein Auto-Encoderscited by 371
  10. 2017MMD GAN: Towards Deeper Understanding of Moment Matching Networkcited by 251
  11. 2018Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Modelcited by 33
  12. 2019How to GAN away Detector Effectscited by 28
3 more
  1. 2020GANplifying Event Samplescited by 28
  2. 2020Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)cited by 20
  3. 2020AI-assisted super-resolution cosmological simulationscited by 15

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 2.

6 papers.

  1. 2022Evaluating generative models in high energy physicscited by 31
  2. 2023Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoningcited by 25
  3. 2022Data Science and Machine Learning in Educationcited by 3
  4. 2023Artificial Intelligence for the Electron Ion Collider (AI4EIC)cited by 3
  5. 2022Physics-Informed Graph Learningcited by 2
  6. 2022Learning Feynman Diagrams using Graph Neural Networkscited by 1