Results 41 to 50 of about 1,338,709 (361)
Frontiers in Environmental Science, 2019 This paper focuses on reducing the computational cost of the Monte Carlo method for uncertainty propagation. Recently, Multi-Fidelity Monte Carlo (MFMC) method (Ng, 2013; Peherstorfer et al., 2016) and Multi-Level Monte Carlo (MLMC) method (Müller et al.,
Nagoor Kani Jabarullah Khan +1 more
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The Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo [PDF]
, 2017 From its inception in the 1950s to the modern frontiers of applied statistics, Markov chain Monte Carlo has been one of the most ubiquitous and successful methods in statistical computing. The development of the method in that time has been fueled by not
M. Betancourt
semanticscholar +1 more source
Coupled Electron Ion Monte Carlo Calculations of Atomic Hydrogen [PDF]
, 2004 We present a new Monte Carlo method which couples Path Integral for finite temperature protons with Quantum Monte Carlo for ground state electrons, and we apply it to metallic hydrogen for pressures beyond molecular dissociation.
Ceperley, David M. +2 more
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Optimization of ground and excited state wavefunctions and van der Waals clusters [PDF]
, 2000 A quantum Monte Carlo method is introduced to optimize excited state trial wavefunctions. The method is applied in a correlation function Monte Carlo calculation to compute ground and excited state energies of bosonic van der Waals clusters of upto seven
Andrei Mushinski +24 more
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Monte Carlo and Quasi Monte Carlo Approach to Ulam's Method for Position Dependent Random Maps
Communications in Advanced Mathematical Sciences, 2020 We consider position random maps $T=\{\tau_1(x),\tau_2(x),\ldots, \tau_K(x); p_1(x),p_2(x),\ldots,p_K(x)\}$ on $I=[0, 1],$ where $\tau_k, k=1, 2, \dots, K$ is non-singular map on $[0,1]$ into $[0, 1]$ and $\{p_1(x),p_2(x),\ldots,p_K(x)\}$ is a set of
Md Shafiqul Islam
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Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey [PDF]
WIREs Computational Statistics, 2020 Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data in the natural world. Monte Carlo (MC) method is a sampling‐based approach
Jiaxin Zhang
semanticscholar +1 more source
Density-matrix quantum Monte Carlo method [PDF]
, 2013 We present a quantum Monte Carlo method capable of sampling the full density matrix of a many-particle system at finite temperature. This allows arbitrary reduced density matrix elements and expectation values of complicated nonlocal observables to be ...
N. S. Blunt +3 more
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Química Nova, 2008 The paper presents an introductory and general discussion on the quantum Monte Carlo methods, some fundamental algorithms, concepts and applicability. In order to introduce the quantum Monte Carlo method, preliminary concepts associated with Monte Carlo ...
Wagner Fernando Delfino Angelotti +3 more
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A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX [PDF]
, 2010 In this work we illustrate the POWHEG BOX, a general computer code framework for implementing NLO calculations in shower Monte Carlo programs according to the POWHEG method.
S. Alioli, P. Nason, C. Oleari, E. Re
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, 2013 The Euler-Maruyama scheme is known to diverge strongly and numerically weakly when applied to nonlinear stochastic differential equations (SDEs) with superlinearly growing and globally one-sided Lipschitz continuous drift coefficients.
Hutzenthaler, Martin +2 more
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