Results 91 to 100 of about 831,537 (291)
Optimized monte carlo methods [PDF]
, 2008 I discuss optimized data analysis and Monte Carlo methods. Reweighting methods are discussed through examples, like Lee-Yang zeroes in the Ising model and the absence of deconfinement in QCD. I discuss reweighted data analysis and multi-hystogramming. I introduce Simulated Tempering, and as an example its application to the Random Field Ising Model.
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Advanced Materials, EarlyView.We report a multifunctional tapered optical fiber integrating a conformal micro‐resistance temperature detector (µRTD) for local, real‐time thermometry during optical stimulation. The platform combines light‐delivery and temperature sensing within a minimally invasive footprint, enabling detection of sub‐degree cortical heating under representative ...
Antonio Balena +6 more
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Application of Monte Carlo simulation methods in risk management
Journal of Business Economics and Management, 2007 The paper deals with Monte Carlo simulation method and its application in Risk Management. The author with the help of MATLAB 7.0 introduces new modification of Monte Carlo algorithm aimed at fast and effective calculation of financial organization's ...
Alexander Suhobokov
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Beyond Earth: Resilience of Quasi‐2D Perovskite Solar Cells in Space
Advanced Materials, EarlyView.In the article (DOI: 10.1002/adma.202520433), Christoph Putz and co‐workers demonstrate rigid quasi‐2D perovskite solar cells operating in low Earth orbit, delivering stable power for more than 100 days under real‐space conditions. In‐orbit performance is correlated with extensive ground‐based thermal and proton‐irradiation studies on rigid and ...
Christoph Putz +17 more
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Monte Carlo Solutions for Blind Phase Noise Estimation
EURASIP Journal on Wireless Communications and Networking, 2009 This paper investigates the use of Monte Carlo sampling methods for phase noise estimation on additive white Gaussian noise (AWGN) channels. The main contributions of the paper are (i) the development of a Monte Carlo framework for phase noise estimation,
Frederik Simoens +4 more
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The paradigm of complex probability and Monte Carlo methods
Systems Science & Control Engineering, 2019 In 1933, Andrey Nikolaevich Kolmogorov established the system of five axioms that define the concept of mathematical probability. This system can be developed to include the set of imaginary numbers and this by adding a supplementary three original ...
Abdo Abou Jaoude
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Multilevel Monte Carlo methods for ensemble variational data assimilation [PDF]
Nonlinear Processes in GeophysicsEnsemble variational data assimilation relies on ensembles of forecasts to estimate the background error covariance matrix B. The ensemble can be provided by an ensemble of data assimilations (EDA), which runs independent perturbed data assimilation and ...
M. Destouches +10 more
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Monte Carlo and kinetic Monte Carlo methods
, 2009 This article reviews the basic computational techniques for carrying out multi-scale simulations using statistical methods, with the focus on simulations of epitaxial growth. First, the statistical-physics background behind Monte Carlo simulations is briefly described.
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AIP Conference Proceedings, 2003 Polymer simulations make extensive use of biased Monte Carlo schemes. In this paper, I describe a subset of polymer‐simulation algorithms that aim to increase the sampling efficiency by biasing the selection of trial moves. Algorithms that belong to this category are the Configurational Bias MC method (CBMC), Dynamical Pruned Enriched Rosenbluth ...
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Advanced Materials, EarlyView.The field of polymer thermoelectrics is entering a new era, featuring breakthroughs in addressing the conventional performance disparity between p‐type and n‐type polymers, pioneering doping frontiers, and sophisticated decoupling strategies. This review explores innovations in molecular design and superior stabilities, bridging the gap from ...
Suhao Wang
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