Premium
An efficient Monte Carlo simulation strategy based on model order reduction and artificial neural networks
Author(s) -
Bamer Franz,
Koeppe Arnd,
Markert Bernd
Publication year - 2017
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201710113
Subject(s) - monte carlo method , artificial neural network , computer science , kinetic monte carlo , monte carlo molecular modeling , reduction (mathematics) , nonlinear system , artificial intelligence , mathematics , markov chain monte carlo , physics , statistics , geometry , quantum mechanics
In this paper, we present a new approach that enables the Monte Carlo simulation of complex nonlinear systems with considerably less computational effort compared to the classical Monte Carlo simulation. Hereby, we propose a combination of the proper orthogonal decomposition and neural networks. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom