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Epic-ly fast particle cloud generation with flow-matching and diffusion

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

citation-role summary

background 1 method 1

citation-polarity summary

fields

hep-ph 3 cs.LG 1

years

2026 3 2024 1

verdicts

UNVERDICTED 4

representative citing papers

Generative models on phase space

hep-ph · 2026-04-02 · unverdicted · novelty 8.0

Generative diffusion and flow models are constructed to remain exactly on the Lorentz-invariant massless N-particle phase space manifold during sampling for particle physics applications.

Local Conformal Predictions for Calibrated Surrogates

hep-ph · 2026-07-01 · unverdicted · novelty 7.0

FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.

EventFlow: Forecasting Temporal Point Processes with Flow Matching

cs.LG · 2024-10-09 · unverdicted · novelty 6.0

EventFlow applies flow matching to learn joint distributions over event times for temporal point processes, reporting 20-53% lower forecast error than autoregressive baselines on standard TPP benchmarks with fewer sampling calls.

citing papers explorer

Showing 4 of 4 citing papers.

  • Generative models on phase space hep-ph · 2026-04-02 · unverdicted · none · ref 25

    Generative diffusion and flow models are constructed to remain exactly on the Lorentz-invariant massless N-particle phase space manifold during sampling for particle physics applications.

  • Local Conformal Predictions for Calibrated Surrogates hep-ph · 2026-07-01 · unverdicted · none · ref 29

    FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.

  • Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms hep-ph · 2026-05-18 · unverdicted · none · ref 18 · 2 links

    Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-global logarithm resummation in the large-Nc limit.

  • EventFlow: Forecasting Temporal Point Processes with Flow Matching cs.LG · 2024-10-09 · unverdicted · none · ref 6

    EventFlow applies flow matching to learn joint distributions over event times for temporal point processes, reporting 20-53% lower forecast error than autoregressive baselines on standard TPP benchmarks with fewer sampling calls.