Results 91 to 100 of about 186,149 (308)

Multilevel Monte Carlo simulation in options pricing [PDF]

, 2014
>Magister Scientiae - MScIn Monte Carlo path simulations, which are used extensively in computational -finance, one is interested in the expected value of a quantity which is a functional of the solution to a stochastic differential equation [M.B. Giles,
Kazeem, Funmilayo Eniola
core  

Disorder‐Induced Extremely Low Thermal Conductivity of Graphite Fluoride

Advanced Science, EarlyView.
This article investigates the effect of fluorination on the thermal conductivity of graphite fluoride, demonstrating the relationship between structural modification and thermal conductivity in an extreme case. The record‐low through‐plane thermal conductivity measured in exfoliated graphite fluoride flakes was found to correlate with the wide ...
Wonsik Lee, Donghoon Moon, Ziyan Qian, Jinwoo Kim, Changheon Kim, Yeojin Lee, Hyobin Yoo, Richard B. Wilson, Qiye Zheng, Gwan‐Hyoung Lee, Hyejin Jang   +10 more
wiley   +1 more source

On Determination Method for Resolution of Secondary Electron Images in Scanning Electron Microscopy

Advanced Science, EarlyView.
An idealized SEM, termed Rayleigh's microscope, is constructed by Monte Carlo simulation to represent imaging conditions that just satisfy the Rayleigh criterion. Based on this physically defined model, sharpness–resolution conversion curves are established and combined with the Rose criterion, enabling automated resolution evaluation from practical ...
Tongfang Yang, Yanbo Zou, Zejun Ding
wiley   +1 more source

Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods

, 2020
We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters.
Tommi, Karkkainen, Paavo, Nieminen, Antti, Pihlajamaki, Hannu, Hakkinen, Joonas, Hamalainen, Sami, Malola, Joakim, Linja   +6 more
core   +1 more source

Efficiency of Static Knowledge Bias in Monte-Carlo Tree Search [PDF]

, 2014
Monte-Carlo methods are currently the best known algorithms for the game of Go. It is already known that Monte-Carlo simulations based on a probability model containing static knowledge of the game are more efficient than random simulations.
Ikeda, Kokolo, Viennot, Simon, Simon Viennot, Kokolo Ikeda   +3 more
core   +1 more source

Hierarchically Soft Porous MOF‐Polymer Monolith for Fast and Large‐Scale Moisture Buffering

Advanced Science, EarlyView.
A soft, hierarchical porous monolith that combines metal–organic frameworks (MOFs) with a thermoresponsive polymer matrix enables rapid, large‐scale moisture buffering. The synergistic interface facilitates high‐capacity water capture and low‐energy release for sustainable indoor dehumidification.
Guangxin Ma, Xin Zhou, Weiman Li, Ken‐ichi Otake, Susumu Kitagawa, Xiaoze Wang, Mengjie Cao, Linfeng Nie, Ming‐Shui Yao, Yunfa Chen   +9 more
wiley   +1 more source

Monte Carlo Simulations of Spin Glasses on Cell Broadband Engine

, 2009
Several large-scale computational scientific problems require high-end computing systems to be solved. In the recent years, design of multi-core architectures delivers on a single chip tens or hundreds Gflops of peak computing performance, with high ...
Belletti, Francesco
core  

Permeability of Fractal Porous Media by Monte Carlo Simulations

, 2005
The permeability of the fractal porous media is simulated by Monte Carlo technique in this work. Based oil the fractal character of pore size distribution in porous media, the probability models for pore diameter and for permeability are derived.
Yu BM(郁伯铭), Zou MQ, Feng YJ, Yu, BM (reprint author), Huazhong Univ Sci & Technol, Dept Phys, 1037 Luoyu Rd, Wuhan 430074, Peoples R China.   +3 more
core   +1 more source

CRANKITE: a fast polypeptide backbone conformation sampler [PDF]

, 2008
Background: CRANKITE is a suite of programs for simulating backbone conformations of polypeptides and proteins. The core of the suite is an efficient Metropolis Monte Carlo sampler of backbone conformations in continuous three-dimensional space in atomic
Wild, David L., David L Wild, Podtelezhnikov, Alexei A., Alexei A Podtelezhnikov   +3 more
core   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source