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Adaptive meshes have the potential to improve the accuracy and efficiency of atmospheric modelling by increasing resolution where it is most needed. Mesh re-distribution, or r-adaptivity, adapts by moving the mesh without changing the…

Numerical Analysis · Mathematics 2022-05-11 Hiroe Yamazaki , Hilary Weller , Colin J. Cotter , Philip A. Browne

This work develops a dynamic homogenization approach for metamaterials. It finds an approximate macroscopic homogenized equation with constant coefficients posed in space and time; however, the resulting homogenized equation is higher order…

Analysis of PDEs · Mathematics 2022-06-23 Kshiteej Deshmukh , Timothy Breitzman , Kaushik Dayal

In order to reduce the computational cost of the simulation of electromagnetic responses in geophysical settings that involve highly heterogeneous media, we develop a multiscale finite volume method with oversampling for the quasi-static…

Computational Physics · Physics 2016-10-10 Luz Angelica Caudillo Mata , Eldad Haber , Christoph Schwarzbach

Many spatial processes exhibit nonstationary features. We estimate a variance function from a single process observation where the errors are nonstationary and correlated. We propose a difference-based approach for a one-dimensional…

Methodology · Statistics 2016-05-24 Eunice J. Kim , Zhengyuan Zhu

In this paper we provide new methodology for inference of the geometric features of a multivariate density in deconvolution. Our approach is based on multiscale tests to detect significant directional derivatives of the unknown density at…

Methodology · Statistics 2016-11-21 Konstantin Eckle , Nicolai Bissantz , Holger Dette

Introducing flexibility in the time-discretisation mesh can improve convergence and computational time when solving differential equations numerically, particularly when the solutions are discontinuous, as commonly found in control problems…

Optimization and Control · Mathematics 2023-06-27 Lucian Nita , Eduardo M. G. Vila , Marta A. Zagorowska , Eric C. Kerrigan , Yuanbo Nie , Ian McInerney , Paola Falugi

Solving inverse and optimization problems over solutions of nonlinear partial differential equations (PDEs) on complex spatial domains is a long-standing challenge. Here we introduce a method that parameterizes the solution using spectral…

Numerical Analysis · Mathematics 2025-10-30 James V. Roggeveen , Michael P. Brenner

The purpose of this research is to describe an efficient iterative method suitable for obtaining high accuracy solutions to high frequency time-harmonic scattering problems. The method allows for both refinement of local polynomial degree…

Computational Physics · Physics 2018-12-26 Ryan Galagusz , Steve McFee

This paper presents an evaluation of efficient radial basis function mesh deformation for complex iced geometries. Given the high computational cost of mesh deformation, state-of-the-art radial basis function techniques are used for data…

Numerical Analysis · Mathematics 2021-07-30 Myles Morelli , Tommaso Bellosta , Alberto Guardone

We reexamine the recently introduced basis-set correction theory based on density-functional theory consisting in correcting the basis-set incompleteness error of wave-function methods using a density functional. We use a one-dimensional…

Chemical Physics · Physics 2022-02-16 Diata Traore , Emmanuel Giner , Julien Toulouse

It is a common practice to evaluate probability density function or matter spatial density function from statistical samples. Kernel density estimation is a frequently used method, but to select an optimal bandwidth of kernel estimation,…

Methodology · Statistics 2021-04-27 Zhen-Wei Li , Ping He

In this paper we propose methods for inference of the geometric features of a multivariate density. Our approach uses multiscale tests for the monotonicity of the density at arbitrary points in arbitrary directions. In particular, a…

Statistics Theory · Mathematics 2016-04-18 Konstantin Eckle , Nicolai Bissantz , Holger Dette , Katharina Proksch , Sabrina Einecke

We investigate density estimation from a $n$-sample in the Euclidean space $\mathbb R^D$, when the data is supported by an unknown submanifold $M$ of possibly unknown dimension $d < D$ under a reach condition. We study nonparametric kernel…

Statistics Theory · Mathematics 2020-11-02 Clément Berenfeld , Marc Hoffmann

A common numerical task is to represent functions which are highly spatially anisotropic, and to solve differential equations related to these functions. One way such anisotropy arises is that information transfer along one spatial…

Numerical Analysis · Mathematics 2017-01-04 Ben F McMillan

In this paper, we study the reconstruction of a bivariate function from weighted integrals along the edges of a triangular mesh, a problem of central importance in tomography, computer vision, and numerical approximation. Our approach…

Numerical Analysis · Mathematics 2025-11-11 Francesco Dell'Accio , Allal Guessab , Mohammed Kbiri Alaoui , Federico Nudo

The influence function in peridynamic material models has a large effect on the dynamic behavior of elastic waves and in turn can greatly effect dynamic simulations of fracture propagation and material failure. Typically, the influence…

Computational Engineering, Finance, and Science · Computer Science 2020-03-13 Xiao Xu , John T. Foster

We use a simple effective model, obtained through the application of high-frequency homogenisation, to tackle the fundamental question of how the choice of gradient function affects the performance of a graded metamaterial. This approach…

Classical Physics · Physics 2025-02-10 Bryn Davies , Lili Fehértói-Nagy , Henry J. Putley

An adaptive moving mesh finite difference method is presented to solve two types of equations with dynamic capillary pressure term in porous media. One is the non-equilibrium Richards Equation and the other is the modified Buckley-Leverett…

Computational Physics · Physics 2017-10-11 Hong Zhang , Paul Andries Zegeling

This paper presents a Bayesian sampling approach to bandwidth estimation for the local linear estimator of the regression function in a nonparametric regression model. In the Bayesian sampling approach, the error density is approximated by…

Methodology · Statistics 2020-11-10 Han Lin Shang , Xibin Zhang

The density hypothesis on random times becomes now a standard in modeling of risks. One of the basic reasons to introduce the density hypothesis is the desire to have a computable credit risk model. However, recent work shows that merely an…

Probability · Mathematics 2014-02-04 Shiqi Song