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We use approximate Bayesian computation (ABC) combined with an "improved" Markov chain Monte Carlo (IMCMC) method to estimate posterior distributions of model parameters in subgrid-scale (SGS) closures for large eddy simulations (LES) of…

Fluid Dynamics · Physics 2020-05-29 Olga A. Doronina , Colin A. Z. Towery , Peter E. Hamlington

Large particle systems are often described by high-dimensional (linear) kinetic equations that are simulated using Monte Carlo methods for which the asymptotic convergence rate is independent of the dimensionality. Even though the…

We present predictions for the statistical error due to finite sampling in the presence of thermal fluctuations in molecular simulation algorithms. Specifically, we establish how these errors depend on Mach number, Knudsen number, number of…

Statistical Mechanics · Physics 2009-11-07 Nicolas Hadjiconstantinou , Alejandro L. Garcia , Martin Z. Bazant , Gang He

The quantum Monte Carlo methods represent a powerful and broadly applicable computational tool for finding very accurate solutions of the stationary Schroedinger equation for atoms, molecules, solids and a variety of model systems. The…

Computational Physics · Physics 2011-01-28 Jindrich Kolorenc , Lubos Mitas

We implemented a fast Reciprocal Monte Carlo algorithm, to accurately solve radiative heat transfer in turbulent flows of non-grey participating media that can be coupled to fully resolved turbulent flows, namely to Direct Numerical…

Computational Engineering, Finance, and Science · Computer Science 2019-05-21 Simone Silvestri , Rene Pecnik

Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at…

Fluid Dynamics · Physics 2022-04-27 Dmitrii Kochkov , Jamie A. Smith , Ayya Alieva , Qing Wang , Michael P. Brenner , Stephan Hoyer

We introduce a multiscale Monte Carlo algorithm to simulate dense simple fluids. The probability of an update follows a power law distribution in its length scale. The collective motion of clusters of particles requires generalization of…

Statistical Mechanics · Physics 2009-11-11 A. C. Maggs

Research on transport, self-assembly and defect dynamics within confined, flowing liquid crystals requires versatile and computationally efficient mesoscopic algorithms to account for fluctuating nematohydrodynamic interactions. We present…

Soft Condensed Matter · Physics 2016-06-22 Tyler N. Shendruk , Julia M. Yeomans

The shape evolution of two-dimensional islands under electromigration-driven periphery diffusion is studied by kinetic Monte Carlo (KMC) simulations and continuum theory. The energetics of the KMC model is adapted to the Cu(100) surface,…

Materials Science · Physics 2015-06-25 Marko Rusanen , Philipp Kuhn , Joachim Krug

A new one-dimensional fluid model for ions in weakly-ionized plasma is proposed. The model differs from the existing ones in two aspects. First, a more accurate approximation of the collision terms in the fluid equations is suggested. For…

Plasma Physics · Physics 2017-04-20 I. L. Semenov

A macroscopic mesoscopic, deterministic stochastic coupling strategy is proposed to accelerate the direct simulation Monte Carlo (DSMC) method for chemical reaction. First, a macroscopic synthetic equation is formulated by integrating…

Computational Physics · Physics 2026-05-14 Hong Deng , Liyan Luo , Lei Wu

We develop new multilevel Monte Carlo (MLMC) methods to estimate the expectation of the smallest eigenvalue of a stochastic convection-diffusion operator with random coefficients. The MLMC method is based on a sequence of finite element…

Numerical Analysis · Mathematics 2024-02-13 Tiangang Cui , Hans De Sterck , Alexander D. Gilbert , Stanislav Polishchuk , Robert Scheichl

Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by…

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to compute the expectation of additive functionals recursively.…

Methodology · Statistics 2010-12-27 Pierre Del Moral , Arnaud Doucet , Sumeetpal Singh

Kinetic approaches, i.e., methods based on the lattice Boltzmann equations, have long been recognized as an appealing alternative for solving incompressible Navier-Stokes equations in computational fluid dynamics. However, such approaches…

Graphics · Computer Science 2018-07-09 Wei Li , Kai Bai , Xiaopei Liu

We investigate hydrodynamic noise in a dilute granular gas during the homogeneous cooling state, by means of a proper application of the Direct Simulation Monte Carlo (DSMC) algorithm. The DSMC includes a source of randomization which is…

Soft Condensed Matter · Physics 2010-09-27 Giulio Costantini , Andrea Puglisi

We present a new algorithm for radiative transfer-based on a statistical Monte Carlo approach-that does not suffer from teleportation effects, on the one hand, and yields smooth results, on the other hand. Implicit Monte Carlo (IMC)…

Instrumentation and Methods for Astrophysics · Physics 2022-01-12 Elad Steinberg , Shay I. Heizler

Owing to their favorable scaling with dimensionality, Monte Carlo (MC) methods have become the tool of choice for numerical integration across the quantitative sciences. Almost invariably, efficient MC integration schemes are strictly…

Statistical Mechanics · Physics 2010-01-29 Artur B. Adib

The present study employs the Direct Simulation Monte Carlo (DSMC) method, a widely used numerical method for studying non-continuum flows, to investigate the flow characteristics of a chemically reacting hypersonic flow over a…

Fluid Dynamics · Physics 2021-12-01 Arjun Singh

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 differential equations…