Neural Network-Based Approach to Phase Space Integration

26 October 2018

Papers citing "Neural Network-Based Approach to Phase Space Integration"

10 / 10 papers shown

Title
Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning Sanghwan Bae Jiwoo Hong Min Young Lee Hanbyul Kim Jeongyeon Nam Donghyun Kwak OffRL LRM 58 4 0 04 Apr 2025
Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows Thorsten Buss F. Gaede Gregor Kasieczka Claudius Krause David Shih AI4CE 38 6 0 30 May 2024
Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm L. Bull P. Gardner T. Rogers N. Dervilis E. Cross E. Papatheou A. E. Maguire C. Campos K. Worden 23 12 0 30 Nov 2021
Generative Networks for Precision Enthusiasts A. Butter Theo Heimel Sander Hummerich Tobias Krebs Tilman Plehn Armand Rousselot Sophia Vent AI4CE 21 59 0 22 Oct 2021
Optimising simulations for diphoton production at hadron colliders using amplitude neural networks Joseph Aylett-Bullock S. Badger Ryan Moodie 13 22 0 17 Jun 2021
Understanding Event-Generation Networks via Uncertainties Marco Bellagente Manuel Haussmann Michel Luchmann Tilman Plehn BDL 41 55 0 09 Apr 2021
A Living Review of Machine Learning for Particle Physics Matthew Feickert Benjamin Nachman KELM AI4CE 31 178 0 02 Feb 2021
Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates Tim Cofala Lars Elend Philip Mirbach J. Prellberg Thomas Teusch Oliver Kramer 30 6 0 06 May 2020
i-flow: High-dimensional Integration and Sampling with Normalizing Flows Christina Gao J. Isaacson Claudius Krause AI4CE 24 107 0 15 Jan 2020
How to GAN away Detector Effects Marco Bellagente A. Butter Gregor Kasieczka Tilman Plehn R. Winterhalder GAN 21 86 0 01 Dec 2019