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Monte Carlo methods are widely used in particle physics to integrate and sample probability distributions (differential cross sections or decay rates) on multi-dimensional phase spaces.
A New Algorithm for Adaptive Multidimensional Integration ,
G. Lepage, · 1978
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VEGAS: AN ADAPTIVE MULTIDIMENSIONAL INTEGRATION PROGRAM (1980)
G. Lepage, · 1980
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Weight optimization in multichannel Monte Carlo ,
R. Kleiss and R. Pittau, · 1994
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i-flow: High-dimensional integration and sampling with normalizing flows (2020), 2001.05486
C. Gao, J. Isaacson and C. Krause, · 2001
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AMEGIC++ 1.0: A Matrix element generator in C++ ,
F. Krauss, R. Kuhn and G. Soff, · 2002
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MadEvent: Automatic event generation with MadGraph ,
F. Maltoni and T. Stelzer, · 2003
Cited alongside, same era.
Foam: A General purpose cellular Monte Carlo event generator ,
S. Jadach, · 2003
Cited alongside, same era.
The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations ,
J. Alwall, R. Frederix, S. Frixione, V. Hirschi, F. Maltoni, O. Mattelaer, H. S. Shao, T. Stelzer, P. Torrielli and M. Zaro, · 2014
Cited alongside, same era.
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems ,
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang and Z. Zhang, · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization ,
D. P. Kingma and J. Ba, · 2015
Cited alongside, same era.
Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks (2017),
J. Bendavid, · 2017
Later among the works it cites.
Reweighting a parton shower using a neural network: the final-state case ,
E. Bothmann and L. Debbio, · 2019
Closest in time.
Exploring phase space with neural importance sampling ,
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale and S. Schumann, · 2020
Closest in time.
Event generation with normalizing flows ,
C. Gao, S. Höche, J. Isaacson, C. Krause and H. Schulz, · 2020
Closest in time.
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