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We present the first Green's function Monte Carlo calculations of light nuclei with nuclear interactions derived from chiral effective field theory up to next-to-next-to-leading order. Up to this order, the interactions can be constructed…

Nuclear Theory · Physics 2014-11-11 J. E. Lynn , J. Carlson , E. Epelbaum , S. Gandolfi , A. Gezerlis , A. Schwenk

Monte Carlo particle transport codes are well established on classical hardware and are considered as the reference tool for nuclear applications. In a growing number of domains, the design of algorithms is progressively shifting towards…

Quantum Physics · Physics 2024-10-28 Noé Olivier , Michel Nowak

Ultracold atomic systems have been of great research interest in the past, with more recent attention being paid to systems of mixed species. In this work we carry out non-perturbative Path Integral Monte Carlo (PIMC) simulations of N…

Quantum Gases · Physics 2017-10-19 William G. Dawkins , Alexandros Gezerlis

In recent years the Swap Monte Carlo algorithm has led to remarkable progress in equilibrating supercooled model liquids at low temperatures. Applications have so far been limited to systems composed of spherical particles, however, whereas…

Soft Condensed Matter · Physics 2025-09-05 Till Böhmer , Jeppe C. Dyre , Lorenzo Costigliola

We present path integral Monte Carlo simulation results for the equation of state of solid parahydrogen between $ 0.024 \, {\r{A}}^{-3} $ and $ 0.1 \, {\r{A}}^{-3} $ at $ T = 4.2 \, $ K. The simulations are performed using non-additive…

Chemical Physics · Physics 2025-06-09 Alexander Ibrahim , Pierre-Nicholas Roy

A new method for sequence optimization in protein models is presented. The approach, which has inherited its basic philosophy from recent work by Deutsch and Kurosky [Phys. Rev. Lett. 76, 323 (1996)] by maximizing conditional probabilities…

Soft Condensed Matter · Physics 2009-10-30 Anders Irbäck , Carsten Peterson , Frank Potthast , Erik Sandelin

The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to…

Computational Physics · Physics 2015-05-27 John C. Quinn , Henry D. I. Abarbanel

The nested sampling algorithm has been shown to be a general method for calculating the pressure-temperature-composition phase diagrams of materials. While the previous implementation used single-particle Monte Carlo moves, these are…

Statistical Mechanics · Physics 2017-11-17 Robert J. N. Baldock , Noam Bernstein , K. Michael Salerno , Lívia B. Pártay , Gábor Csányi

We give an introduction to the calculation of path integrals on a lattice, with the quantum harmonic oscillator as an example. In addition to providing an explicit computational setup and corresponding pseudocode, we pay particular…

Computational Physics · Physics 2018-04-03 Marise J. E. Westbroek , Peter R. King , Dimitri D. Vvedensky , Stephan Durr

An ab-initio method for determining the dynamical structure function of an interacting many--body quantum system has been devised by combining a generalized integral transform method with Quantum Monte Carlo methods. As a first application,…

Other Condensed Matter · Physics 2012-09-26 Alessandro Roggero , Francesco Pederiva , Giuseppina Orlandini

Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop a novel approach to reduce this…

Computational Physics · Physics 2020-07-10 Callum M. Macdonald , Simon Arridge , Samuel Powell

Quantum Monte Carlo is one of the most promising approaches for dealing with large-scale quantum many-body systems. It has played an extremely important role in understanding strongly correlated physics. However, two fundamental problems,…

Strongly Correlated Electrons · Physics 2026-03-03 Zhiyan Wang , Zenan Liu , Bin-Bin Mao , Zhe Wang , Zheng Yan

Clustering of the four-nucleon system at kinetic freezeout conditions is studied using path-integral Monte Carlo techniques. This method seeks to improve upon previous calculations which relied on approximate semiclassical methods or…

Nuclear Theory · Physics 2021-10-27 Dallas DeMartini , Edward Shuryak

Multifidelity Monte Carlo methods rely on a hierarchy of possibly less accurate but statistically correlated simplified or reduced models, in order to accelerate the estimation of statistics of high-fidelity models without compromising the…

Numerical Analysis · Mathematics 2020-10-29 Alessio Quaglino , Simone Pezzuto , Rolf Krause

We introduce a novel learning framework for accelerated Monte Carlo (MC) dose calculation termed Energy-Shifting. This approach leverages deep learning to synthesize highly complex polyenergetic dose distributions directly from simple…

We extend the Worldline Monte Carlo approach to computationally simulating the Feynman path integral of non-relativistic multi-particle quantum-mechanical systems. We show how to generate an arbitrary number of worldlines distributed…

We describe a major upgrade of a Monte Carlo code which has previously been used for many studies of dense star clusters. We outline the steps needed in order to calibrate the results of the new Monte Carlo code against $N$-body simulations…

Astrophysics of Galaxies · Physics 2015-06-03 Mirek Giersz , Douglas C. Heggie , Jarrod Hurley , Arkadiusz Hypki

We perform calculations of the {3D} finite-temperature homogeneous electron gas (HEG) in the warm-dense regime ({r_{s} \equiv (3/4\pi n)^{1/3}a_{B}^{- 1} = 1.0- 40.0} and {\Theta \equiv T/T_{F} = 0.0625- 8.0}) using restricted path integral…

Strongly Correlated Electrons · Physics 2013-04-10 Ethan W. Brown , Bryan K. Clark , Jonathan L. DuBois , David M. Ceperley

It was recently demonstrated that a simple Monte Carlo (MC) algorithm involving the swap of particle pairs dramatically accelerates the equilibrium sampling of simulated supercooled liquids. We propose two numerical schemes integrating the…

Statistical Mechanics · Physics 2019-06-24 Ludovic Berthier , Elijah Flenner , Christopher J. Fullerton , Camille Scalliet , Murari Singh

While multilevel Monte Carlo (MLMC) methods for the numerical approximation of partial differential equations with random coefficients enjoy great popularity, combinations with spatial adaptivity seem to be rare. We present an adaptive MLMC…

Numerical Analysis · Mathematics 2017-12-20 Ralf Kornhuber , Evgenia Youett