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When using finite element and finite difference methods to approximate eigenvalues of $2m^{th}$-order elliptic problems, the number of reliable numerical eigenvalues can be estimated in terms of the total degrees of freedom $N$ in resulting…

Numerical Analysis · Mathematics 2013-12-25 Zhimin Zhang

We consider systems of stochastic evolutionary equations of the $p$-Laplace type. We establish convergence rates for a finite-element based space-time approximation, where the error is measured in a suitable quasi-norm. Under natural…

Analysis of PDEs · Mathematics 2021-05-10 Dominic Breit , Martina Hofmanova , Sebastien Loisel

This paper compares the Maximum-likelihood method and Bayesian method for finite element model updating. The Maximum-likelihood method was implemented using genetic algorithm while the Bayesian method was implemented using the Markov Chain…

Applications · Statistics 2007-05-23 Tshilidzi Marwala , Lungile Mdlazi , Sibusiso Sibisi

We introduce a numerical framework to verify the finite step convergence of first-order methods for parametric convex quadratic optimization. We formulate the verification problem as a mathematical optimization problem where we maximize a…

Optimization and Control · Mathematics 2025-04-18 Vinit Ranjan , Bartolomeo Stellato

This paper develops new insights into quantitative methods for the validation of computational model prediction. Four types of methods are investigated, namely classical and Bayesian hypothesis testing, a reliability-based method, and an…

Data Analysis, Statistics and Probability · Physics 2012-06-25 You Ling , Sankaran Mahadevan

For many applications, it is critical to interpret and validate groups of observations obtained via clustering. A common validation approach involves testing differences in feature means between observations in two estimated clusters. In…

Methodology · Statistics 2023-11-29 Yiqun T. Chen , Lucy L. Gao

In this paper we establish a best approximation property of fully discrete Galerkin finite element solutions of second order parabolic problems on convex polygonal and polyhedral domains in the $L^\infty$ norm. The discretization method…

Numerical Analysis · Mathematics 2018-08-20 Dmitriy Leykekhman , Boris Vexler

In this paper, finite element method is applied to Leland's model for numerical simulation of option pricing with transaction costs. Spatial finite element models based on P1 and/or P2 elements are formulated in combination with a…

Computational Finance · Quantitative Finance 2020-10-27 Dongming Wei , Yogi Ahmad Erlangga , Gulzat Zhumakhanova

Based on a new Taylor-like formula, we derived an improved interpolation error estimate in $W^{1,p}$. We compare it with the classical error estimates based on the standard Taylor formula, and also with the corresponding interpolation error…

Numerical Analysis · Mathematics 2023-10-31 Joel Chaskalovic , Franck Assous

We present a weak finite element method for elliptic problems in one space dimension. Our analysis shows that this method has more advantages than the known weak Galerkin method proposed for multi-dimensional problems, for example, it has…

Numerical Analysis · Mathematics 2016-06-29 Tie Zhang , Yanli Chen

The numerical analysis for the small amplitude motion of an elastic beam with internal damping is investigated in domain with moving ends. An efficient numerical method is constructed to solve this moving boundary problem. The stability and…

Numerical Analysis · Mathematics 2019-07-05 Natanael Quintino , Mauro Rincon

I present a new and reliable method to test the numerical accuracy of NLO calculations based on modern OPP/Generalized Unitarity techniques. A convenient solution to rescue most of the detected numerically inaccurate points is also…

High Energy Physics - Phenomenology · Physics 2010-10-14 R. Pittau

When using the finite element method (FEM) in inverse problems, its discretization error can produce parameter estimates that are inaccurate and overconfident. The Bayesian finite element method (BFEM) provides a probabilistic model for the…

Numerical Analysis · Mathematics 2026-01-26 Anne Poot , Iuri Rocha , Pierre Kerfriden , Frans van der Meer

Error estimates are proved for finite element approximations to the solution of second-order hyperbolic partial differential equations with coefficients varying in both space and time. Optimal rates of convergence in the energy norm are…

Numerical Analysis · Mathematics 2026-03-17 Oussama Al Jarroudi , Marcus J. Grote

We discuss and analyze the virtual element method on general polygonal meshes for the time-dependent Poisson-Nernst-Planck equations, which are a nonlinear coupled system widely used in semiconductors and ion channels. The spatial…

Numerical Analysis · Mathematics 2022-07-18 Ying Yang , Ya Liu , Shi Shu

A reliable method for characterizing quantum operations that is suitable for improving and validating their accuracies is indispensable for realizing a practical quantum computer. Known methods are still not sufficient because they lack…

Quantum Physics · Physics 2021-06-25 Takanori Sugiyama , Shinpei Imori , Fuyuhiko Tanaka

Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate…

Econometrics · Economics 2024-06-19 Matthew D. Cocci , Mikkel Plagborg-Møller

Variational-hemivariational inequalities are an important mathematical framework for nonsmooth problems. The framework can be used to study application problems from physical sciences and engineering that involve non-smooth and even…

Numerical Analysis · Mathematics 2025-03-10 Weimin Han , Fang Feng , Fei Wang , Jianguo Huang

In this paper, we employ a space-time finite element method to discretize the parabolic initial-boundary value problem and extend its error analysis with refined estimates on unstructured space-time meshes. We establish higher-order…

Numerical Analysis · Mathematics 2025-03-13 Thi Thanh Mai Ta , Quang Huy Nguyen , Phi Hung Pham

This paper introduces a new technique for quantifying the approximation error of a broad class of probabilistic inference programs, including ones based on both variational and Monte Carlo approaches. The key idea is to derive a subjective…

Artificial Intelligence · Computer Science 2016-06-02 Marco F Cusumano-Towner , Vikash K Mansinghka