相关论文: Algorithms and parameters for improved accuracy in…
Population Monte Carlo simulations in the form commonly referred to as population annealing can serve as a useful meta-algorithm for simulating systems with complex free-energy landscapes. In the present paper we provide an easily…
An efficient Path Integral Monte Carlo procedure is proposed to simulate the behavior of quantum many-body dissipative systems described within the framework of the influence functional. Thermodynamic observables are obtained by Monte Carlo…
A multi-platform validation and analysis framework for public Monte Carlo simulation for high-energy particle collisions is discussed. The front-end of this framework uses the Python programming language, while the back-end is written in…
Several Monte Carlo algorithms and applications that are useful for understanding the concepts of temperature and chemical potential are discussed. We then introduce a generalization of the demon algorithm that measures the chemical…
In this work, we explore the use of an iterative Bayesian Monte Carlo (IBM) procedure for nuclear data evaluation within a Talys Evaluated Nuclear data Library (TENDL) framework. In order to identify the model and parameter combinations…
We assess the ability of future neutron beta decay measurements of up to O(10^{-4}) precision to falsify the standard model, particularly the V-A law, and to identify the dynamics beyond it. To do this, we employ a maximum likelihood…
A Monte Carlo program which simulates the response of SiPMs is presented. Input to the program are the mean number and the time distribution of Geiger discharges from light, as well as the dark-count rate. For every primary Geiger discharge…
Recent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for…
The procedures of validating simulation of particle physics events at the LHC are summarized. Because of the strongly fluctuating particle content of LHC events and detector interactions, particle based Monte Carlo methods are an…
The theory of radiative transfer provides the link between the physical conditions in an astrophysical object and the observable radiation which it emits. Thus accurately modelling radiative transfer is often a necessary part of testing…
A digital hadronic calorimeter using MICROMEGAS as active elements is a very promising choice for particle physics experiments at future lepton colliders. These experiments will be optimized for application of the particle flow algorithm…
Accurately and efficiently estimating system performance under uncertainty is paramount in power system planning and operation. Monte Carlo simulation is often used for this purpose, but convergence may be slow, especially when detailed…
Nuclei will play a prominent role in searches for physics beyond the Standard Model as the active material in experiments. In order to reliably interpret new physics signals, one needs an accurate model of the underlying nuclear dynamics.…
We present a method to facilitate Monte Carlo simulations in the grand canonical ensemble given a target mean particle number. The method imposes a fictitious dynamics on the chemical potential, to be run concurrently with the Monte Carlo…
Gaussian Process (GP) models are often used as mathematical approximations of computationally expensive experiments. Provided that its kernel is suitably chosen and that enough data is available to obtain a reasonable fit of the simulator,…
Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics, information engineering and signal processing. Particle methods, also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical…
New explicit velocity- and position-Verlet-like algorithms of the second order are proposed to integrate the equations of motion in many-body systems. The algorithms are derived on the basis of an extended decomposition scheme at the…
Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based model…
The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems…
We study approximations of evolving probability measures by an interacting particle system. The particle system dynamics is a combination of independent Markov chain moves and importance sampling/resampling steps. Under global regularity…