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Atomistic simulations provide valuable insights into the physical processes governing material behavior. However, their applicability is fundamentally constrained by the limited time scales accessible to brute-force simulations. This…

Computational Physics · Physics 2026-02-16 Michael Kim , Wei Cai

Irreversible and rejection-free Monte Carlo methods, recently developed in Physics under the name Event-Chain and known in Statistics as Piecewise Deterministic Monte Carlo (PDMC), have proven to produce clear acceleration over standard…

Computation · Statistics 2020-04-28 Manon Michel , Alain Durmus , Stéphane Sénécal

In this work we compare Monte Carlo (MC) simulations of electron transport properties with reflection electron energy loss measurements in diamond and graphite films. We assess the impact of different approximations of the dielectric…

We present density response estimators for Monte Carlo simulations that are based on a reweighting procedure, where the samples of an unperturbed system are used to estimate the properties of a system perturbed by an external harmonic…

Monte Carlo (MC) simulation is commonly considered to be the most accurate dose calculation method in radiotherapy. However, its efficiency still requires improvement for many routine clinical applications. In this paper, we present our…

Medical Physics · Physics 2015-05-28 Xun Jia , Xuejun Gu , Yan Jiang Graves , Michael Folkerts , Steve B. Jiang

Monte Carlo simulation provides a powerful tool for understanding and exploring thermodynamic phase equilibria in many-particle interacting systems. Among the most physically intuitive simulation methods is Gibbs ensemble Monte Carlo…

Statistical Mechanics · Physics 2015-06-12 Alan R. Denton , Michael P. Schmidt

A new approach to study the response of portable gamma detector to terrestrial gamma ray is proposed. This approach is based on two-stage Monte Carlo simulation. First, the probability distributions of the phase space coordinates of the…

Instrumentation and Detectors · Physics 2015-06-09 Boubaker Askri

A Monte Carlo method is presented to evaluate quantum states with many particles moving in the continuum. The scattering state is generated at each time by a Monte Carlo random sampling algorithm. The same calculation are repeated until the…

Nuclear Theory · Physics 2013-06-06 Zhen-Xiang Xu , Chong Qi

The recently developed auxiliary field diffusion Monte Carlo method is applied to compute the equation of state and the compressibility of neutron matter. By combining diffusion Monte Carlo for the spatial degrees of freedom and auxiliary…

Nuclear Theory · Physics 2009-11-10 A. Sarsa , S. Fantoni , K. E. Schmidt , F. Pederiva

The real-space variation quantum Monte Carlo (VMC) and diffusion quantum Monte Carlo (DMC) are used to calculate the quasiparticle energy bands and the quasiparticle effective mass of the paramagnetic and ferromagnetic two-dimensional…

Strongly Correlated Electrons · Physics 2025-11-07 S. Azadi , N. D. Drummond , A. Principi , R. V. Belosludov , M. S. Bahramy

We briefly review the principles, mathematical bases, numerical shortcuts and applications of fast random walk (FRW) algorithms. This Monte Carlo technique allows one to simulate individual trajectories of diffusing particles in order to…

Computational Physics · Physics 2013-05-01 Denis Grebenkov

The shell-model Monte Carlo (SMMC) technique transforms the traditional nuclear shell-model problem into a path-integral over auxiliary fields. We describe below the method and its applications to four physics issues: calculations of sdpf-…

Nuclear Theory · Physics 2009-10-31 D. J. Dean , J. A. White

Correlated fermions are of high interest in condensed matter (Fermi liquids, Wigner molecules), cold atomic gases and dense plasmas. Here we propose a novel approach to path integral Monte Carlo (PIMC) simulations of strongly degenerate…

Quantum Gases · Physics 2016-01-15 Tobias Dornheim , Simon Groth , Alexey Filinov , Michael Bonitz

The increasing interest in environmentally friendly gas mixtures for gaseous particle detectors, especially tetrafluoropropene-based gas mixtures for Resistive Plate Chambers (RPCs), has prompted the need for simulating electron transport…

Instrumentation and Detectors · Physics 2023-04-21 Antonio Bianchi

The computer revolution has been driven by a sustained increase of computational speed of approximately one order of magnitude (a factor of ten) every five years since about 1950. In natural sciences this has led to a continuous increase of…

Statistical Mechanics · Physics 2007-09-06 Bernd A. Berg

This chapter is devoted to the computation of equilibrium (thermodynamic) properties of quantum systems. In particular, we will be interested in the situation where the interaction between particles is so strong that it cannot be treated as…

Mesoscale and Nanoscale Physics · Physics 2016-02-03 Alexei Filinov , Jens Böning , Michael Bonitz

The following electromagnetism (EM) inverse problem is addressed. It consists in estimating local radioelectric properties of materials recovering an object from the global EM scattering measurement, at various incidences and wave…

Applications · Statistics 2015-06-11 François Giraud , Pierre Minvielle , Marc Sancandi , Pierre Del Moral

Precise knowledge of the static density response function (SDRF) of the uniform electron gas (UEG) serves as key input for numerous applications, most importantly for density functional theory beyond generalized gradient approximations.…

Strongly Correlated Electrons · Physics 2017-11-06 Simon Groth , Tobias Dornheim , Michael Bonitz

Monte Carlo simulation code has been developed and tested for studying the passage of charged particle beams and radiation through the crystalline matter at energies from tens of MeV up to hundreds of GeV. The developed Monte Carlo code…

Computational Physics · Physics 2007-05-23 Armen Apyan

Variational Monte Carlo (VMC) is an approach for computing ground-state wavefunctions that has recently become more powerful due to the introduction of neural network-based wavefunction parametrizations. However, efficiently training neural…

Machine Learning · Statistics 2023-10-03 Robert J. Webber , Michael Lindsey
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