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This paper extends the Multilevel Monte Carlo variance reduction technique to nonlinear filtering. In particular, Multilevel Monte Carlo is applied to a certain variant of the particle filter, the Ensemble Transform Particle Filter. A key…

Numerical Analysis · Mathematics 2016-02-24 Alastair Gregory , Colin Cotter , Sebastian Reich

We develop a formalism and present an algorithm for optimization of the trial wave-function used in fixed-node diffusion quantum Monte Carlo (DMC) methods. We take advantage of a basic property of the walker configuration distribution…

Strongly Correlated Electrons · Physics 2015-05-13 Fernando A. Reboredo , Randolph Q. Hood , Paul R. C. Kent

The self-healing diffusion Monte Carlo algorithm (SHDMC) [Reboredo, Hood and Kent, Phys. Rev. B {\bf 79}, 195117 (2009); Reboredo, {\it ibid.} {\bf 80}, 125110 (2009)] is extended to study the ground and excited states of magnetic and…

Strongly Correlated Electrons · Physics 2011-06-10 Fernando Agustín Reboredo

We present novel Monte Carlo methods for treating the interacting shell model that allow exact calculations much larger than those heretofore possible. The two-body interaction is linearized by an auxiliary field; Monte Carlo evaluation of…

Nuclear Theory · Physics 2008-11-26 C. W. Johnson , S. E. Koonin , G. H. Lang , W. E. Ormand

We present a Monte Carlo method to compute efficiently susceptibilites or covariances of two physical variables. The method relies on a generalization of the exchange cluster algorithm to any model of interacting particles with any $2$-body…

Computational Physics · Physics 2025-02-11 Assaraf Roland , Chevreau Hilaire

Finding high-quality trial wave functions for quantum Monte Carlo calculations of light nuclei requires a strong intuition for modeling the interparticle correlations as well as large computational resources for exploring the space of…

Nuclear Theory · Physics 2026-05-29 Pengsheng Wen , Alexandros Gezerlis , Jeremy W. Holt

We modify the reweighting factor of the projector used in diffusion Monte Carlo to reduce the time-step error of the total energy. Further, we present a reweighting scheme that has the desirable feature that it is exactly size-consistent,…

Chemical Physics · Physics 2024-03-18 Tyler A. Anderson , Manolo C. Per , C. J. Umrigar

This work introduces a novel multilevel Monte Carlo (MLMC) metamodeling approach for variance function estimation. Although devising an efficient experimental design for simulation metamodeling can be elusive, the MLMC-based approach…

Methodology · Statistics 2025-04-22 Jingtao Zhang , Xi Chen

The projective quantum Monte Carlo (PQMC) algorithms are among the most powerful computational techniques to simulate the ground state properties of quantum many-body systems. However, they are efficient only if a sufficiently accurate…

Computational Physics · Physics 2019-10-04 S. Pilati , E. M. Inack , P. Pieri

This paper concerns numerical assessment of Monte Carlo error in particle filters. We show that by keeping track of certain key features of the genealogical structure arising from resampling operations, it is possible to estimate variances…

Computation · Statistics 2016-06-29 Anthony Lee , Nick Whiteley

The electronic properties of the oxygen molecule, in its singlet and triplet states, and of many small oxygen-containing radicals and anions have important roles in different fields of Chemistry, Biology and Atmospheric Science.…

Chemical Physics · Physics 2014-07-04 Andrea Zen , Bernhardt L. Trout , Leonardo Guidoni

The first three dynamic multipole polarizabilities for the ground state of hydrogen, helium, hydride ion, and positronium hydride PsH have been computed using the variational Monte Carlo (VMC) method and explicitly correlated wave…

Atomic Physics · Physics 2009-11-06 Massimo Mella , Dario Bressanini , Gabriele Morosi

A new computational method for finite-temperature properties of strongly correlated electrons is proposed by extending the variational Monte Carlo method originally developed for the ground state. The method is based on the path integral in…

Strongly Correlated Electrons · Physics 2016-06-10 Kensaku Takai , Kota Ido , Takahiro Misawa , Youhei Yamaji , Masatoshi Imada

Ab initio quantum Monte Carlo (QMC) is a state-of-the-art numerical approach for evaluating accurate expectation values of many-body wavefunctions. However, one of the major drawbacks that still hinders widespread QMC applications is the…

Materials Science · Physics 2024-07-17 Kousuke Nakano , Michele Casula , Giacomo Tenti

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To…

High Energy Physics - Phenomenology · Physics 2024-11-08 Prasanth Shyamsundar , Jacob L. Scott , Stephen Mrenna , Konstantin T. Matchev , Kyoungchul Kong

In a previous work [L.Delle Site, J.Phys.A 40, 2787 (2007)] the derivation of an analytic expression for the kinetic functional of a many-body electron system has been proposed. Though analytical, the formula is still non local…

Quantum Physics · Physics 2009-11-13 Luca M. Ghiringhelli , Luigi Delle Site

Metastability is a formidable challenge to Markov chain Monte Carlo methods. In this paper we present methods for algorithm design to meet this challenge. The design problem we consider is temperature selection for the infinite swapping…

Probability · Mathematics 2020-11-12 Paul Dupuis , Guo-Jhen Wu

Monte Carlo techniques play a central role in statistical mechanics approaches for connecting macroscopic thermodynamic and kinetic properties to the electronic structure of a material. This paper describes the implementation of Monte Carlo…

Materials Science · Physics 2023-09-22 Brian Puchala , John C. Thomas , Anton Van der Ven

By using a full many-body approach, we calculate the excitation energy, the effective mass and the density profile of soliton states in a three dimensional Bose gas of hard spheres at zero temperature. The many-body wave function used to…

Quantum Gases · Physics 2015-11-05 Riccardo Rota , Stefano Giorgini

In predictive modeling with simulation or machine learning, it is critical to accurately assess the quality of estimated values through output analysis. In recent decades output analysis has become enriched with methods that quantify the…

Methodology · Statistics 2023-10-27 Kimia Vahdat , Sara Shashaani
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