scholarly journals Controlling the model sign problem via the path optimization method: Monte Carlo approach to a QCD effective model with Polyakov loop

2019 ◽  
Vol 99 (1) ◽  
Author(s):  
Kouji Kashiwa ◽  
Yuto Mori ◽  
Akira Ohnishi
Keyword(s):  
Monte Carlo ◽  
Optimization Method ◽  
Polyakov Loop ◽  
Path Optimization ◽  
Effective Model ◽  
Monte Carlo Approach ◽  
Sign Problem

scholarly journals Toward solving the sign problem with path optimization method

2017 ◽  
Vol 96 (11) ◽  
Author(s):  
Yuto Mori ◽  
Kouji Kashiwa ◽  
Akira Ohnishi
Keyword(s):  
Optimization Method ◽  
Path Optimization ◽  
Sign Problem

scholarly journals Path optimization method for the sign problem

2018 ◽  
Vol 175 ◽  
pp. 07043 ◽  
Author(s):  
Akira Ohnishi ◽  
Yuto Mori ◽  
Kouji Kashiwa
Keyword(s):  
Optimization Method ◽  
Singular Points ◽  
Path Optimization ◽  
Real Field ◽  
Integration Path ◽  
Flow Equations ◽  
The Neural Network ◽  
Sign Problem ◽  
Path Integral Method ◽  
The One

We propose a path optimization method (POM) to evade the sign problem in the Monte-Carlo calculations for complex actions. Among many approaches to the sign problem, the Lefschetz-thimble path-integral method and the complex Langevin method are promising and extensively discussed. In these methods, real field variables are complexified and the integration manifold is determined by the flow equations or stochastically sampled. When we have singular points of the action or multiple critical points near the original integral surface, however, we have a risk to encounter the residual and global sign problems or the singular drift term problem. One of the ways to avoid the singular points is to optimize the integration path which is designed not to hit the singular points of the Boltzmann weight. By specifying the one-dimensional integration-path as z = t +if(t)(f ϵ R) and by optimizing f(t) to enhance the average phase factor, we demonstrate that we can avoid the sign problem in a one-variable toy model for which the complex Langevin method is found to fail. In this proceedings, we propose POM and discuss how we can avoid the sign problem in a toy model. We also discuss the possibility to utilize the neural network to optimize the path.

scholarly journals Path optimization in $0+1$D QCD at finite density

2019 ◽  
Vol 2019 (11) ◽  
Author(s):  
Yuto Mori ◽  
Kouji Kashiwa ◽  
Akira Ohnishi
Keyword(s):  
Neural Network ◽  
Quantum Chromodynamics ◽  
Gauge Theories ◽  
Chemical Potential ◽  
Optimization Method ◽  
Feedforward Neural Network ◽  
Path Optimization ◽  
Sign Problem ◽  
Gauge Fixing ◽  
Link Variable

Abstract We investigate the sign problem in $0+1$D quantum chromodynamics at finite chemical potential by using the path optimization method. The SU(3) link variable is complexified to the SL(3,$\mathbb{C}$) link variable, and the integral path is represented by a feedforward neural network. The integral path is then optimized to weaken the sign problem. The average phase factor is enhanced to be greater than 0.99 on the optimized path. Results with and without diagonalized gauge fixing are compared and proven to be consistent. This is the first step in applying the path optimization method to gauge theories.

The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development

2020 ◽  
Vol 20 (14) ◽  
pp. 1389-1402 ◽  
Author(s):  
Maja Zivkovic ◽  
Marko Zlatanovic ◽  
Nevena Zlatanovic ◽  
Mladjan Golubović ◽  
Aleksandar M. Veselinović
Keyword(s):  
Monte Carlo ◽  
Molecular Docking ◽  
Computer Calculation ◽  
Molecular Graph ◽  
Optimization Method ◽  
Docking Studies ◽  
Optimization Approach ◽  
Qsar Modeling ◽  
Monte Carlo Optimization ◽  
Molecular Docking Studies

In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization approach as conformation independent method, has emerged. Monte Carlo optimization has proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery and design. In this review, the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from the molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization-based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/ decrease of biological activity, which are used further to design compounds with targeted activity based on computer calculation, are presented. In this mini-review, research papers in which molecular docking was applied as an additional method to design molecules to validate their activity further, are summarized. These papers present a very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.

Wigner-Boltzmann Monte Carlo approach to nanodevice simulation: from quantum to semiclassical transport

2009 ◽  
Vol 8 (3-4) ◽  
pp. 324-335 ◽  
Author(s):  
Damien Querlioz ◽  
Huu-Nha Nguyen ◽  
Jérôme Saint-Martin ◽  
Arnaud Bournel ◽  
Sylvie Galdin-Retailleau ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Approach ◽  
Semiclassical Transport
Sign in / Sign up

Export Citation Format

Share Document