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Path integral Monte Carlo (PIMC) simulations are used to calculate the momentum distribution of the homogeneous electron gas at finite temperature. This is done by calculating the off-diagonal elements of the real-space density matrix,…

Statistical Mechanics · Physics 2007-05-23 B. Militzer , E. L. Pollock , D. M. Ceperley

In two-dimensional random waves, phase singularities are point-like dislocations with a behavior reminiscent of interacting particles. This -- qualitative -- consideration, stems from the spatial arrangement of these entities, which finds…

Optics · Physics 2021-06-04 L. De Angelis , L. Kuipers

We present a new Monte Carlo method which couples Path Integral for finite temperature protons with Quantum Monte Carlo for ground state electrons, and we apply it to metallic hydrogen for pressures beyond molecular dissociation. This…

Statistical Mechanics · Physics 2009-11-10 Markus Holzmann , Carlo Pierleoni , David M. Ceperley

We consider a discrete-time system of n coupled random vectors, a.k.a. interacting particles. The dynamics involve a vanishing step size, some random centered perturbations, and a mean vector field which induces the coupling between the…

Probability · Mathematics 2025-06-09 Pascal Bianchi , Walid Hachem , Victor Priser

Unlike with the energy, which is a scalar property, machine learning (ML) predictions of vector or tensor properties poses the additional challenge of achieving proper invariance (covariance) with respect to molecular rotation. If the…

Chemical Physics · Physics 2025-08-13 Jakub Martinka , Marek Pederzoli , Mario Barbatti , Pavlo O. Dral , Jiří Pittner

To the present day, the Beenakker-Mazur (BM) method is the most comprehensive statistical physics approach to the calculation of short-time transport properties of colloidal suspensions. A revised version of the BM method with an improved…

Soft Condensed Matter · Physics 2015-09-17 Karol Makuch , Marco Heinen , Gustavo C. Abade , Gerhard Nägele

The deep reinforcement learning (DRL) based Volt-VAR optimization (VVO) methods have been widely studied for active distribution networks (ADNs). However, most of them lack safety guarantees in terms of power injection uncertainties due to…

Systems and Control · Electrical Eng. & Systems 2024-09-30 Zhengrong Chen , Siyao Cai , A. P. Sakis Meliopoulos

Improvements beyond the primitive approximation in the path integral Monte Carlo method are explored both in a model problem and in real systems. Two different strategies are studied: the Richardson extrapolation on top of the path integral…

Statistical Mechanics · Physics 2009-11-10 L. Brualla , K. Sakkos , J. Boronat , J. Casulleras

For emerging applications of hybrid pixel detectors which require high spatial resolution, e.g., subpixel interpolation in X-ray imaging and deep learning-based electron localization, accurate modeling of charge transport processes in the…

Computer modeling of multicellular systems has been a valuable tool for interpreting and guiding in vitro experiments relevant to embryonic morphogenesis, tumor growth, angiogenesis and, lately, structure formation following the printing of…

Biological Physics · Physics 2015-05-28 Elijah Flenner , Lorant Janosi , Bogdan Barz , Adrian Neagu , Gabor Forgacs , Ioan Kosztin

We study numerical integration over bounded regions in $\mathbb{R}^s, s\ge1$ with respect to some probability measure. We replace random sampling with quasi-Monte Carlo methods, where the underlying point set is derived from deterministic…

Numerical Analysis · Mathematics 2023-05-01 Tiangang Cui , Josef Dick , Friedrich Pillichshammer

Ultracold atomic gases are, to a very good approximation, described by pairwise zero-range interactions. This paper demonstrates that $N$-body systems with two-body zero-range interactions can be treated reliably and efficiently by the…

Quantum Gases · Physics 2015-08-04 Yangqian Yan , D. Blume

We propose a new sensitivity analysis methodology for complex stochastic dynamics based on the Relative Entropy Rate. The method becomes computationally feasible at the stationary regime of the process and involves the calculation of…

Mathematical Physics · Physics 2013-04-16 Yannis Pantazis , Markos A. Katsoulakis

In this master thesis, a new approximation scheme to non-relativistic potential scattering is developed and discussed. The starting points are two exact path integral representations of the T-matrix, which permit the application of the…

Nuclear Theory · Physics 2010-01-15 Julien Carron

Density ratio estimation (DRE) is a paramount task in machine learning, for its broad applications across multiple domains, such as covariate shift adaptation, causal inference, independence tests and beyond. Parametric methods for…

Machine Learning · Statistics 2025-06-03 Meilin Wang , Wei Huang , Mingming Gong , Zheng Zhang

Recently, some works have suggested methods to combine variational probabilistic inference with Monte Carlo sampling. One promising approach is via local optimal transport. In this approach, a gradient steepest descent method based on local…

Machine Learning · Statistics 2019-01-30 Manuel Pulido , Peter Jan vanLeeuwen , Derek J. Posselt

Recently Carlon et. al. investigated the critical behavior of the pair contact process with diffusion [cond-mat/9912347]. Using density matrix renormalization group methods, they estimate the critical exponents, raising the possibility that…

Statistical Mechanics · Physics 2009-10-31 Haye Hinrichsen

We have developed a path integral Monte Carlo method for simulating helium films and apply it to the second layer of helium adsorbed on graphite. We use helium-helium and helium-graphite interactions that are found from potentials which…

Condensed Matter · Physics 2009-10-31 Marlon Pierce , Efstratios Manousakis

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

Reaction rates are a complicated function of molecular interactions, which can be selected from vast chemical design spaces. Seeking the design that optimizes a rate is a particularly challenging problem since the rate calculation for any…

Statistical Mechanics · Physics 2020-11-18 Alex Albaugh , Todd R. Gingrich