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This paper proposes an original adaptive refinement framework using Radial Basis Functions-generated Finite Differences method. Node distributions are generated with a Poisson Disk Sampling-based algorithm from a given continuous density…

Numerical Analysis · Mathematics 2020-05-12 Jure Slak , Gregor Kosec

We introduce a filtering technique for Discontinuous Galerkin approximations of hyperbolic problems. Following an approach already proposed for the Hamilton-Jacobi equations by other authors, we aim at reducing the spurious oscillations…

Numerical Analysis · Mathematics 2023-05-18 Giuseppe Orlando

The Taylor expansion of wave fields with respect to shape parameters has a wide range of applications in wave scattering problems, including inverse scattering, optimal design, and uncertainty quantification. However, deriving the high…

Numerical Analysis · Mathematics 2025-03-24 Gang Bao , Haoran Ma , Jun Lai , Jingzhi Li

We present high order accurate numerical methods for the wave equation that combines efficient Hermite methods with eometrically flexible discontinuous Galerkin methods by using overset grids. Near boundaries we use thin boundary fitted…

Numerical Analysis · Mathematics 2020-07-10 Oleksii Beznosov , Daniel Appelö

In this paper, a novel Hermite radial basis function-based differential quadrature method (H-RBF-DQ) is presented. This new method is designed to treat derivative boundary conditions accurately. The developed method is very different from…

Computational Physics · Physics 2019-03-27 Jianming Liu , Xinkai Li

This paper investigates an adaptive wavelet collocation time domain method for the numerical solution of Maxwell's equations. In this method a computational grid is dynamically adapted at each time step by using the wavelet decomposition of…

Numerical Analysis · Mathematics 2012-04-06 Haojun Li , Kirankumar R. Hiremath , Andreas Rieder , Wolfgang Freude

This paper presents a novel method for polynomial approximation (Hermite approximation) using the fusion of value and derivative information. Therefore, the least-squares error in both domains is simultaneously minimized. A covariance…

Numerical Analysis · Mathematics 2019-03-27 Roland Ritt , Matthew Harker , Paul O'Leary

In this work we propose and analyze a novel Hybrid High-Order discretization of a class of (linear and) nonlinear elasticity models in the small deformation regime which are of common use in solid mechanics. The proposed method is valid in…

Numerical Analysis · Mathematics 2017-07-10 Michele Botti , Daniele Di Pietro , Pierre Sochala

Transcorrelation (TC) techniques effectively enhance convergence rates in strongly correlated fermionic systems by embedding electron-electron cusp into the Jastrow factor of similarity transformations, yielding a non-Hermitian, yet…

Quantum Physics · Physics 2025-03-19 Bruna G. M. Araújo , Antonio M S Macedo

We develop a novel Hybrid High-Order method for the simulation of Darcy flows in fractured porous media. The discretization hinges on a mixed formulation in the bulk region and on a primal formulation inside the fracture. Salient features…

Numerical Analysis · Mathematics 2017-12-01 Florent Chave , Daniele Di Pietro , Luca Formaggia

This paper presents an efficient parallel direct algorithm with near-optimal complexity for the compact fourth and sixth-order approximation of the three-dimensional Helmholtz equations [1] with the problem coefficient depending on only one…

Numerical Analysis · Mathematics 2020-03-13 Ronald Gonzales , Yury Gryazin , Yun Teck Lee

Successive quadratic approximations, or second-order proximal methods, are useful for minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part that promotes regularization. Most analyses of iteration…

Optimization and Control · Mathematics 2019-01-25 Ching-pei Lee , Stephen J. Wright

In this paper I discuss how to consistently incorporate higher-order corrections to the bubble-nucleation rate at finite temperature. Doing so I examine the merits of different approaches, with the goal of reducing uncertainties for…

High Energy Physics - Phenomenology · Physics 2022-03-01 Andreas Ekstedt

In many iterative optimization methods, fixed-point theory enables the analysis of the convergence rate via the contraction factor associated with the linear approximation of the fixed-point operator. While this factor characterizes the…

Systems and Control · Electrical Eng. & Systems 2022-06-22 Trung Vu , Raviv Raich

Many problems in machine learning write as the minimization of a sum of individual loss functions over the training examples. These functions are usually differentiable but, in some cases, their gradients are not Lipschitz continuous, which…

Optimization and Control · Mathematics 2024-04-29 S. Chraibi , F. Iutzeler , J. Malick , A. Rogozin

Hamiltonian systems are known to conserve the Hamiltonian function, which describes the energy evolution over time. Obtaining a numerical spatio-temporal scheme that accurately preserves the discretized Hamiltonian function is often a…

Numerical Analysis · Mathematics 2023-10-10 Anand Srinivasan , Jose E. Castillo

Second order accurate Cartesian grid methods have been well developed for interface problems in the literature. However, it is challenging to develop third or higher order accurate methods for problems with curved interfaces and internal…

Numerical Analysis · Mathematics 2022-06-14 Zhilin Li , Kejia Pan , Juan Ruiz

We develop a fourth order accurate finite difference method for the three dimensional elastic wave equation in isotropic media with the piecewise smooth material property. In our model, the material property can be discontinuous at curved…

Numerical Analysis · Mathematics 2021-06-29 Lu Zhang , Siyang Wang , N. Anders Petersson

This paper presents a robust enhancement of the Tangent space Hermite Interpolation (THI) method for manifold-valued data by integrating the multivariate Arnoldi process. To circumvent the inherent numerical instability of multivariate…

Numerical Analysis · Mathematics 2026-05-26 Yuxuan Li , Qiang Niu , Wubin Zhou

Model order reduction provides low-complexity high-fidelity surrogate models that allow rapid and accurate solutions of parametric differential equations. The development of reduced order models for parametric \emph{nonlinear} Hamiltonian…

Numerical Analysis · Mathematics 2024-09-30 Cecilia Pagliantini , Federico Vismara