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Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems
We are interested in computing the expectation of a functional of a PDE solution under a Bayesian posterior distribution. Using Bayes's rule, we reduce the problem to estimating the ratio of two related prior expectations. For a...
Quasi-Monte Carlo and Multilevel Monte Carlo Methods for Computing Posterior Expectations in Elliptic Inverse Problems
We are interested in computing the expectation of a functional of a PDE solution under a Bayesian posterior distribution. Using Bayes's rule, we reduce the problem to estimating the ratio of two related prior expectations. For a...
Anytime Monte Carlo
Abstract
Monte Carlo algorithms simulates some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose a...
Sphere Encapsulated Monte Carlo
We introduce a simple global optimization approach that is able to find minimum energy configurations of clusters containing aromatic molecules. The translational and rotational perturbations required in Monte Carlo-based...
Analysis of reported error in Monte Carlo rendered images.
Evaluating image quality in Monte Carlo rendered images is an important aspect of the rendering process as we often need to determine the relative quality between images computed using different algorithms and with varying...
Spin correlations in Monte Carlo simulations.
P Richardson
Feb 17, 2010
We show that the algorithm originally proposed by Collins and Knowles
for spin correlations in the QCD parton shower can be used in order to include spin
correlations between the production and decay of heavy particles in Monte...
Published by: HEP, Cavendish Laboratory, University of Cambridge
Transitions between imperfectly ordered crystalline structures
Nigel Wilding
Jan 01, 0001
A model for two-dimensional colloids confined laterally by “structured boundaries” (i.e., ones that impose a periodicity along the slit) is studied by Monte Carlo simulations. When the distance D between the confining walls is...
Adversarial Monte Carlo Denoising with Conditioned Auxiliary Feature Modulation
Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Many previous works, including regression-based and learning-based methods, have been explored to...
SCONE
Over the last decade, the importance of the Monte Carlo as a neutron transport calculation method has greatly increased. This paper describes a Monte Carlo particle transport framework SCONE, which aims to provide with...
Transitions between imperfectly ordered crystalline structures
Nigel Wilding
Jan 01, 0001
A model for two-dimensional colloids confined laterally by “structured boundaries” (i.e., ones that impose a periodicity along the slit) is studied by Monte Carlo simulations. When the distance D between the confining walls is...
Adversarial Monte Carlo Denoising with Conditioned Auxiliary Feature Modulation
Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Many previous works, including regression-based and learning-based methods, have been explored to...
Parton distributions with scale uncertainties
Abstract
We present the MCscales approach for incorporating scale uncertainties in parton distribution functions (PDFs). The new methodology builds on the...
Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies
In this paper, we present a generalization of the multilevel Monte Carlo (MLMC) method to a setting where the level parameter is a continuous variable. This continuous level Monte Carlo (CLMC) estimator provides a natural...
math.NA CLMC Adaptivity Complexity theorem Heterogeneous elliptic PDEs Unbiased estimator multilevel Monte Carlo /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics /dk/atira/pure/subjectarea/asjc/2600/2607 name=Discrete Mathematics and Combinatorics /dk/atira/pure/subjectarea/asjc/2600/2613 name=Statistics and Probability /dk/atira/pure/subjectarea/asjc/1800/1804 name=Statistics, Probability and Uncertainty /dk/atira/pure/subjectarea/asjc/2600/2611 name=Modelling and Simulation
Quasi-Monte Carlo methods for elliptic PDEs with random coefficients and applications
We devise and implement quasi-Monte Carlo methods for computing the expectations of nonlinear functionals of solutions of a class of elliptic partial differential equations with random coefficients. Our motivation comes from...
lattice_mc
Benjamin Morgan
May 26, 2017
lattice_mc is a lattice-gas kinetic Monte Carlo simulation tool, written in Python. The code allows the simulation of particle transport on periodic lattices for non-interacting particles (volume exclusion only), and for...
Unbiased reduced density matrices and electronic properties from full configuration interaction quantum Monte Carlo.
Properties that are necessarily formulated within pure (symmetric) expectation values are difficult to calculate for projector quantum Monte Carlo approaches, but are critical in order to compute many of the important observable...
Quasi-Monte Carlo methods for elliptic PDEs with random coefficients and applications
We devise and implement quasi-Monte Carlo methods for computing the expectations of nonlinear functionals of solutions of a class of elliptic partial differential equations with random coefficients. Our motivation comes from...
A positive-weight next-to-leading-order Monte Carlo for e<sup>+</sup>e <sup>-</sup> annihilation to hadrons
We apply the positive-weight Monte Carlo method of Nason for simulating QCD
processes accurate to Next-To-Leading Order to the case of e+e- annihilation to
hadrons. The method entails the generation of the hardest gluon emission...
Direct evaluation of the force constant matrix in quantum Monte Carlo.
We develop a formalism to directly evaluate the matrix of force constants within a Quantum Monte Carlo calculation. We utilize the matrix of force constants to accurately relax the positions of atoms in molecules and determine...
lattice_mc
Benjamin Morgan
May 26, 2017
lattice_mc is a lattice-gas kinetic Monte Carlo simulation tool, written in Python. The code allows the simulation of particle transport on periodic lattices for non-interacting particles (volume exclusion only), and for...
Fast electrostatic solvers for kinetic Monte Carlo simulations
Kinetic Monte Carlo (KMC) is an important computational tool in theoretical physics and chemistry. In contrast to standard Monte Carlo, KMC permits the description of time dependent dynamical processes and is not restricted...
Domain specific language Electrostatics Fast Multipole Method kinetic Monte Carlo Parallel computing /dk/atira/pure/subjectarea/asjc/2600/2612 name=Numerical Analysis /dk/atira/pure/subjectarea/asjc/2600/2611 name=Modelling and Simulation /dk/atira/pure/subjectarea/asjc/3100/3101 name=Physics and Astronomy (miscellaneous) /dk/atira/pure/subjectarea/asjc/3100/3100 name=General Physics and Astronomy /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/2600/2605 name=Computational Mathematics /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics /dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy name=SDG 7 - Affordable and Clean Energy
A Bayesian approach to Mendelian randomization with multiple pleiotropic variants.
We propose a Bayesian approach to Mendelian randomization (MR), where instruments are allowed to exert pleiotropic (i.e. not mediated by the exposure) effects on the outcome. By having these effects represented in the model by...
Multilevel Monte Carlo and Improved Timestepping Methods in Atmospheric Dispersion Modelling
Grigoris Katsiolides, Eike H. Müller, Robert Scheichl, Tony Shardlow, Michael B. Giles, David J. Thomson
Feb 01, 2018
A common way to simulate the transport and spread of pollutants in the atmosphere is via stochastic Lagrangian dispersion models. Mathematically, these models describe turbulent transport processes with stochastic...
Multilevel Monte Carlo simulation for Lévy processes based on the Wiener–Hopf factorisation
In Kuznetsov et al. (2011) a new Monte Carlo simulation technique was introduced for a large family of Lévy processes that is based on the Wiener–Hopf decomposition. We pursue this idea further by combining their technique with...
Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies
In this paper, we present a generalization of the multilevel Monte Carlo (MLMC) method to a setting where the level parameter is a continuous variable. This continuous level Monte Carlo (CLMC) estimator provides a natural...
math.NA CLMC Adaptivity Complexity theorem Heterogeneous elliptic PDEs Unbiased estimator multilevel Monte Carlo /dk/atira/pure/subjectarea/asjc/2600/2604 name=Applied Mathematics /dk/atira/pure/subjectarea/asjc/2600/2607 name=Discrete Mathematics and Combinatorics /dk/atira/pure/subjectarea/asjc/2600/2613 name=Statistics and Probability /dk/atira/pure/subjectarea/asjc/1800/1804 name=Statistics, Probability and Uncertainty /dk/atira/pure/subjectarea/asjc/2600/2611 name=Modelling and Simulation
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