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For many complex simulation tasks spanning areas such as healthcare, engineering, and finance, Monte Carlo (MC) methods are invaluable due to their unbiased estimates and precise error quantification. Nevertheless, Monte Carlo simulations…

We present the first practical Monte Carlo calculations of the recently proposed Lefschetz thimble formulation of quantum field theories. Our results provide strong evidence that the numerical sign problem that afflicts Monte Carlo…

High Energy Physics - Lattice · Physics 2013-11-15 Marco Cristoforetti , Francesco Di Renzo , Abhishek Mukherjee , Luigi Scorzato

The next generation of cosmological surveys will operate over unprecedented scales, and will therefore provide exciting new opportunities for testing general relativity. The standard method for modelling the structures that these surveys…

General Relativity and Quantum Cosmology · Physics 2018-03-28 Sophia R. Goldberg , Christopher Gallagher , Timothy Clifton

A technique for reducing the number of integrals in a Monte Carlo calculation is introduced. For integrations relying on classical or mean-field trajectories with local weighting functions, it is possible to integrate analytically at least…

Statistical Mechanics · Physics 2024-05-17 Jarod Tall , Steven Tomsovic

We investigate reversibility violations in the Hybrid Monte Carlo algorithm. Those violations are inevitable when computers with finite numerical precision are being used. In SU(2) gauge theory, we study the dependence of observables on the…

High Energy Physics - Lattice · Physics 2018-03-14 Carsten Urbach

Gaussian random fields play an important role in many areas of science and engineering. In practice, they are often simulated by sampling from a high-dimensional multivariate normal distribution, which arises from the discretisation of a…

Numerical Analysis · Mathematics 2026-02-12 Yoshihito Kazashi , Eike H. Müller , Robert Scheichl

We study the feature-scaled version of the Monte Carlo algorithm with linear function approximation. This algorithm converges to a scale-invariant solution, which is not unduly affected by states having feature vectors with large norms. The…

Machine Learning · Computer Science 2022-05-31 Rahul Madhavan , Hemanta Makwana

Monte Carlo studies of many quantum systems face exponentially severe signal-to-noise problems. We show that noise arising from complex phase fluctuations of observables can be reduced without introducing bias using path integral contour…

High Energy Physics - Lattice · Physics 2020-08-05 William Detmold , Gurtej Kanwar , Michael L. Wagman , Neill C. Warrington

We introduce modifications to Monte Carlo simulations of the Feynman path integral that improve sampling of localised interactions. The new algorithms generate trajectories in simple background potentials designed to concentrate them about…

Quantum Physics · Physics 2025-08-06 Ivan Ahumada , James P. Edwards

The Monte Carlo event generators (MC) are used for the simulation of different processes in high energy physics. To achieve the best description of the data, the parameters of simulations are adjusted (tuned) with different methods. In this…

High Energy Physics - Experiment · Physics 2018-01-23 Fabian Klimpel

We consider a scalar field interacting with a quantized metric varying on a submanifold (e.g. a scalar field interacting with a quantized gravitational wave). We explicitly sum up the perturbation series for the time-ordered vacuum…

High Energy Physics - Theory · Physics 2007-05-23 Z. Haba

We describe a Monte Carlo simulation study of the magnetic phase diagram of diluted magnetic semiconductors doped with shallow impurities in the low concentration regime. We show that because of a wide distribution of interaction strengths,…

Disordered Systems and Neural Networks · Physics 2015-06-25 R. N. Bhatt , Xin Wan

We shortly illustrate how the field-theoretic approach to critical phenomena takes place in the more complete Wilson theory of renormalization and qualitatively discuss its domain of validity. By the way, we suggest that the differential…

High Energy Physics - Theory · Physics 2014-11-18 C. Bagnuls , C. Bervillier

We propose sequential Monte Carlo (SMC) methods for sampling the posterior distribution of state-space models under highly informative observation regimes, a situation in which standard SMC methods can perform poorly. A special case is…

Computation · Statistics 2015-07-10 Pierre Del Moral , Lawrence M. Murray

Quantitative theory of interbilayer interactions is essential to interpret x-ray scattering data and to elucidate these interactions for biologically relevant systems. For this purpose Monte Carlo simulations have been performed to obtain…

Biological Physics · Physics 2009-10-31 Nikolai Gouliaev , John F. Nagle

Many problems require to approximate an expected value by some kind of Monte Carlo (MC) sampling, e.g. molecular dynamics (MD) or simulation of stochastic reaction models (also termed kinetic Monte Carlo (kMC)). Often, we are furthermore…

Numerical Analysis · Mathematics 2019-02-18 Sandra Döpking , Sebastian Matera

The concept of conformal field theory provides a general classification of statistical systems on two-dimensional geometries at the point of a continuous phase transition. Considering the finite-size scaling of certain special observables,…

Statistical Mechanics · Physics 2017-09-27 M. Weigel , W. Janke

Numerical simulation is an important non-perturbative tool to study quantum field theories defined in non-commutative spaces. In this contribution, a selection of results from Monte Carlo calculations for non-commutative models is…

High Energy Physics - Theory · Physics 2016-11-21 Marco Panero

These lecture notes introduce quantum spin systems and several computational methods for studying their ground-state and finite-temperature properties. Symmetry-breaking and critical phenomena are first discussed in the simpler setting of…

Strongly Correlated Electrons · Physics 2015-03-17 Anders W. Sandvik

In this paper, we propose an efficient Monte Carlo implementation of non-linear FBSDEs as a system of interacting particles inspired by the ideas of branching diffusion method. It will be particularly useful to investigate large and complex…

Computational Finance · Quantitative Finance 2012-04-24 Masaaki Fujii , Akihiko Takahashi
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