Understanding the CO 2 /CH 4 /N 2 Separation Performance of Nanoporous Amorphous N‐Doped Carbon Combined Hybrid Monte Carlo with Machine Learning

2021 ◽  
pp. 2100378
Author(s):  
Boran Li ◽  
Song Wang ◽  
Ziqi Tian ◽  
Ge Yao ◽  
Hui Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Separation Performance ◽  
Hybrid Monte Carlo

scholarly journals MCNNTUNES: Tuning Shower Monte Carlo generators with machine learning

2021 ◽  
Vol 263 ◽  
pp. 107908
Author(s):  
Marco Lazzarin ◽  
Simone Alioli ◽  
Stefano Carrazza
Keyword(s):  
Machine Learning ◽  
Monte Carlo

scholarly journals The Ising model with Hybrid Monte Carlo

2021 ◽  
Vol 265 ◽  
pp. 107978
Author(s):  
Johann Ostmeyer ◽  
Evan Berkowitz ◽  
Thomas Luu ◽  
Marcus Petschlies ◽  
Ferenc Pittler
Keyword(s):  
Monte Carlo ◽  
Ising Model ◽  
Hybrid Monte Carlo

scholarly journals Testing a Fourier-accelerated hybrid Monte Carlo algorithm

2002 ◽  
Vol 528 (3-4) ◽  
pp. 301-305 ◽  
Author(s):  
Simon Catterall ◽  
Sergey Karamov
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Algorithm ◽  
Hybrid Monte Carlo

A machine learning approach for filtering Monte Carlo noise

2015 ◽  
Vol 34 (4) ◽  
pp. 1-12 ◽  
Author(s):  
Nima Khademi Kalantari ◽  
Steve Bako ◽  
Pradeep Sen
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals COMPARISON OF UPDATE ALGORITHMS FOR PURE GAUGE SU(3)

1988 ◽  
Vol 03 (14) ◽  
pp. 1367-1378 ◽  
Author(s):  
RAJAN GUPTA ◽  
GREGORY W. KILCUP ◽  
APOORVA PATEL ◽  
STEPHEN R. SHARPE ◽  
PHILIPPE DE FORCRAND
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Algorithm ◽  
Hybrid Monte Carlo ◽  
Pure Gauge

We show that the overrelaxed algorithm of Creutz and of Brown and Woch is the optimal local update algorithm for simulation of pure gauge SU(3). Our comparison criterion includes computer efficiency and decorrelation times. We also investigate the rate of decorrelation for the Hybrid Monte Carlo algorithm.

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