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The effectiveness of a new algorithm, parallel tempering, is studied for numerical simulations of biological molecules. These molecules suffer from a rough energy landscape. The resulting slowing down in numerical simulations is overcome by…

Chemical Physics · Physics 2009-10-30 Ulrich H. E. Hansmann

We present in a unified framework new conforming and nonconforming Virtual Element Methods (VEM) for general second order elliptic problems in two and three dimensions. The differential operator is split into its symmetric and non-symmetric…

Numerical Analysis · Mathematics 2015-07-14 Andrea Cangiani , Gianmarco Manzini , Oliver J. Sutton

We introduce vertex block descent, a block coordinate descent solution for the variational form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local vertex position updates that achieve reductions in global…

Graphics · Computer Science 2024-06-04 Anka He Chen , Ziheng Liu , Yin Yang , Cem Yuksel

We introduce a second-order numerical scheme for compressible atmospheric motions at small to planetary scales. The collocated finite volume method treats the advection of mass, momentum, and mass-weighted potential temperature in…

Numerical Analysis · Mathematics 2020-01-08 Tommaso Benacchio , Rupert Klein

We present a new entropy-based moment method for the velocity discretization of kinetic equations. This method is based on a regularization of the optimization problem defining the original entropy-based moment method, and this gives the…

Numerical Analysis · Mathematics 2018-04-17 Graham W. Alldredge , Martin Frank , Cory D. Hauck

We present a methodology for simulating dilute suspensions of particles settling under gravity, with the main purpose of overcoming limitations of triply periodic configurations, mainly the strong vertical correlation that hinders the study…

Fluid Dynamics · Physics 2026-04-24 M. Moriche , M. García-Villalba , M. Uhlmann

We propose new algorithms for numerical integration of the equations of motion for classical spin systems with fixed spatial site positions. The algorithms are derived on the basis of a mid-point scheme in conjunction with the multiple time…

Statistical Mechanics · Physics 2009-10-31 I. P. Omelyan , I. M. Mryglod , R. Folk

A new framework is introduced for constructing interpretable and truly reliable reduced models for multiscale problems in situations without scale separation. Hydrodynamic approximation to the kinetic equation is used as an example to…

Computational Physics · Physics 2019-10-31 Jiequn Han , Chao Ma , Zheng Ma , Weinan E

In this paper, we consider lasso problems with zero-sum constraint, commonly required for the analysis of compositional data in high-dimensional spaces. A novel algorithm is proposed to solve these problems, combining a tailored active-set…

Optimization and Control · Mathematics 2022-09-26 Andrea Cristofari

In this paper a second order dynamical system model is proposed for computing a zero of a maximal comonotone operator in Hilbert spaces. Under mild conditions, we prove existence and uniqueness of a strong global solution of the proposed…

Optimization and Control · Mathematics 2023-07-10 Zengzhen Tan , Rong Hu , Yaping Fang

Variational regularization of ill-posed inverse problems is based on minimizing the sum of a data fidelity term and a regularization term. The balance between them is tuned using a positive regularization parameter, whose automatic choice…

Numerical Analysis · Mathematics 2025-11-12 Markus Juvonen , Bjørn Jensen , Ilmari Pohjola , Yiqiu Dong , Samuli Siltanen

The combined quantum electron-nuclear dynamics is often associated with the Born-Huang expansion of the molecular wave function and the appearance of nonadiabatic effects as a perturbation. On the other hand, native multicomponent…

The choice of the parameter value for regularized inverse problems is critical to the results and remains a topic of interest. This article explores a criterion for selecting a good parameter value by maximizing the probability of the data,…

Numerical Analysis · Mathematics 2020-02-11 Toby Sanders , Rodrigo B. Platte , Robert D. Skeel

The paper presents a novel model order reduction technique for large-scale linear parameter varying (LPV) systems. The approach is based on decoupling the original dynamics into smaller dimensional LPV subsystems that can be independently…

Systems and Control · Computer Science 2016-09-23 T. Luspay , T. Peni , I. Gozse , Z. Szabo , B. Vanek

Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of non-linear systems from experimental datasets. Recently, several attempts have extended DMD to the context of low-rank approximations. This…

Machine Learning · Statistics 2018-05-18 Patrick Héas , Cédric Herzet

We propose a fast method for the calculation of short-range interactions in molecular dynamics simulations. The so-called random-batch list method is a stochastic version of the classical neighbor-list method to avoid the construction of a…

Computational Physics · Physics 2021-08-25 Jiuyang Liang , Zhenli Xu , Yue Zhao

In this paper, we introduce a new image denoising model: the damped flow (DF), which is a second order nonlinear evolution equation associated with a class of energy functionals of image. The existence, uniqueness and regularization…

Numerical Analysis · Mathematics 2019-10-01 George Baravdish , Olof Svensson , Mårten Gulliksson , Ye Zhang

A second order accurate numerical scheme is proposed and implemented for the Landau-Lifshitz-Gilbert equation, which models magnetization dynamics in ferromagnetic materials, with large damping parameters. The main advantages of this method…

Computational Physics · Physics 2022-01-26 Yongyong Cai , Jingrun Chen , Cheng Wang , Changjian Xie

Numerical simulation of compressible fluid flows is performed using the Euler equations. They include the scalar advection equation for the density, the vector advection equation for the velocity and a given pressure dependence on the…

Computational Engineering, Finance, and Science · Computer Science 2018-01-22 Petr N. Vabishchevich

Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function and its derivatives. Here we propose a new parameter estimation technique that does not require computing an intractable…

Machine Learning · Computer Science 2015-03-13 Jascha Sohl-Dickstein , Peter Battaglino , Michael R. DeWeese