mlsanityData to 14 Oct 2026

Generative Models and Statistical Validation

arXiv 2605.30453 ↗First posted 28 May 2026hep-ph cs.LG physics.data-ancited by 2

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Abstract (arXiv)

Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 76 of its 157 references that are in the map. First posted on arXiv 28 May 2026.

Builds on

76 of 157 references.

References · 76

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2022Flow Matching for Generative Modelingcited by 5,702
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  6. 2018Demystifying MMD GANscited by 1,293
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  9. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  10. 2020Anomaly Detection with Density Estimationcited by 72
  11. 2017Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeterscited by 55
  12. 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
38 more
  1. 2021Particle Cloud Generation with Message Passing Generative Adversarial Networkscited by 46
  2. 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inferencecited by 46
  3. 2024OmniJet-α: The first cross-task foundation model for particle physicscited by 43
  4. 2019Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physicscited by 42
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  6. 2024CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulationcited by 35
  7. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
  8. 2022Evaluating generative models in high energy physicscited by 31
  9. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 30
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  11. 2020GANplifying Event Samplescited by 28
  12. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  13. 2019LHC analysis-specific datasets with Generative Adversarial Networkscited by 26
  14. 2020i-flow: High-dimensional Integration and Sampling with Normalizing Flowscited by 26
  15. 2018Generative Models for Fast Calorimeter Simulation.LHCb casecited by 25
  16. 2023Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoningcited by 25
  17. 2023Diffusion Models as Stochastic Quantization in Lattice Field Theorycited by 24
  18. 2023EPiC-GAN: Equivariant Point Cloud Generation for Particle Jetscited by 23
  19. 2023Normalizing flows for lattice gauge theory in arbitrary space-time dimensioncited by 23
  20. 2023End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physicscited by 22
  21. 2021Generative Networks for Precision Enthusiastscited by 21
  22. 2022Flow-based sampling in the lattice Schwinger model at criticalitycited by 20
  23. 2022Posterior samples of source galaxies in strong gravitational lenses with score-based priorscited by 20
  24. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
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  26. 2017CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networkscited by 17
  27. 2022Probabilistic Mass Mapping with Neural Score Estimationcited by 17
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  29. 2023Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulationcited by 16
  30. 2024Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulationcited by 15
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  32. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
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  35. 2023Goodness of fit by Neyman-Pearson testingcited by 10
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  38. 2024Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciencescited by 9

50 most cited of 76 All 76 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthroughcited by 2
  2. 2026VERaiPHY -- Validation & Evaluation for Robust AI in PHYsicscited by 0