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We derive an explicit $k-$dependence in $W^{m,p}$ error estimates for $P_k$ Lagrange finite elements. Two laws of probability are established to measure the relative accuracy between $P_{k_1}$ and $P_{k_2}$ finite elements ($k_1 < k_2$) in…

Numerical Analysis · Mathematics 2019-09-10 Joel Chaskalovic , Franck Assous

The aim of this paper is to provide a new perspective on finite element accuracy. Starting from a geometrical reading of the Bramble-Hilbert lemma, we recall the two probabilistic laws we got in previous works that estimate the relative…

Numerical Analysis · Mathematics 2019-02-13 Joël Chaskalovic , Franck Assous

The aim of this paper is to provide new perspectives on relative finite element accuracy which is usually based on the asymptotic speed of convergence comparison when the mesh size $h$ goes to zero. Starting from a geometrical reading of…

Numerical Analysis · Mathematics 2019-01-14 Joël Chaskalovic , Franck Assous

The aim of this paper is to provide new perspectives on the relative finite elements accuracy. Starting from a geometrical interpretation of the error estimate which can be deduced from Bramble-Hilbert lemma, we derive a probability law…

Numerical Analysis · Mathematics 2019-01-14 Joel Chaskalovic , Franck Assous

In this paper we propose a new generation of probability laws based on the generalized Beta prime distribution to estimate the relative accuracy between two Lagrange finite elements $P_{k_1}$ and $P_{k_2}, (k_1<k_2)$. Since the relative…

Numerical Analysis · Mathematics 2020-11-24 Joel Chaskalovic , Franck Assous

A probabilistic approach is developed for the exact solution $u$ to a determinist partial differential equation as well as for its associated approximation $u^{(k)}_{h}$ performed by $P_k$ Lagrange finite element. Two limitations motivated…

Numerical Analysis · Mathematics 2020-02-06 Joel Chaskalovic

To obtain the highest confidence on the correction of numerical simulation programs for the resolution of Partial Differential Equations (PDEs), one has to formalize the mathematical notions and results that allow to establish the soundness…

Logic in Computer Science · Computer Science 2024-10-03 François Clément , Vincent Martin

We introduce probability estimation, a broadly applicable framework to certify randomness in a finite sequence of measurement results without assuming that these results are independent and identically distributed. Probability estimation…

Quantum Physics · Physics 2018-11-30 Yanbao Zhang , Emanuel Knill , Peter Bierhorst

The recently proposed statistical finite element (statFEM) approach synthesises measurement data with finite element models and allows for making predictions about the unknown true system response. We provide a probabilistic error analysis…

Statistics Theory · Mathematics 2025-06-17 Toni Karvonen , Fehmi Cirak , Mark Girolami

We present a novel probabilistic finite element method (FEM) for the solution and uncertainty quantification of elliptic partial differential equations based on random meshes, which we call random mesh FEM (RM-FEM). Our methodology allows…

Numerical Analysis · Mathematics 2021-06-17 Assyr Abdulle , Giacomo Garegnani

In this work, we present a study combining two approaches in the context of solving PDEs: the continuous finite element method (FEM) and more recent techniques based on neural networks. In recent years, physics-informed neural networks…

In this paper, we examine a finite element approximation of the steady $p(\cdot)$-Navier-Stokes equations ($p(\cdot)$ is variable dependent) and prove orders of convergence by assuming natural fractional regularity assumptions on the…

Numerical Analysis · Mathematics 2024-11-15 Luigi C. Berselli , Alex Kaltenbach

In this paper we study the finite element approximation of systems of $p(\cdot)$-Stokes type, where $p(\cdot)$ is a (non constant) given function of the space variables. We derive --in some cases optimal-- error estimates for finite element…

Numerical Analysis · Mathematics 2017-01-03 Luigi C. Berselli , Dominic Breit , Lars Diening

We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such…

Numerical Analysis · Mathematics 2016-02-17 Philipp Hennig , Michael A Osborne , Mark Girolami

This paper presents a probabilistic model validation methodology for nonlinear systems in time-domain. The proposed formulation is simple, intuitive, and accounts both deterministic and stochastic nonlinear systems with parametric and…

Systems and Control · Computer Science 2014-02-04 Abhishek Halder , Raktim Bhattacharya

Quantum-enhanced measurements exploit quantum mechanical effects to provide ultra-precise estimates of physical variables for use in advanced technologies, such as frequency calibration of atomic clocks, gravitational waves detection, and…

Quantum Physics · Physics 2014-07-28 J. Calsamiglia , B. Gendra , R. Munoz-Tapia , E. Bagan

In this article, we discuss a couple of nonlinear Galerkin methods (NLGM) in finite element set up for time dependent incompressible Navier-Sotkes equations. We show the crucial role played by the non-linear term in determining the rate of…

Numerical Analysis · Mathematics 2013-06-14 Deepjyoti Goswami

In this paper, we present convergence theorems for numerical solutions of the incompressible Euler equations. The first result is the Lax-Wendroff-type theorem, while the second can be formulated in the framework of the Lax equivalence…

Numerical Analysis · Mathematics 2026-04-02 Mária Lukáčová-Medviďová , Bangwei She

In a general setting, we study a posteriori estimates used in finite element analysis to measure the error between a solution and its approximation. The latter is not necessarily generated by a finite element method. We show that the error…

Numerical Analysis · Mathematics 2025-07-09 Thomas Führer , Sergio Rojas

The proximal Galerkin (PG) method is a finite element method for solving variational problems with inequality constraints. It has several advantages, including constraint-preserving approximations and mesh independence. This paper presents…

Numerical Analysis · Mathematics 2026-02-09 Brendan Keith , Rami Masri , Marius Zeinhofer
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