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This work concerns linearization methods for efficiently solving the Richards` equation,a degenerate elliptic-parabolic equation which models flow in saturated/unsaturated porous media.The discretization of Richards` equation is based on…

Numerical Analysis · Mathematics 2017-06-01 Florian List , Florin Adrian Radu

We develop a randomized Newton's method for solving differential equations, based on a fully connected neural network discretization. In particular, the randomized Newton's method randomly chooses equations from the overdetermined nonlinear…

Numerical Analysis · Mathematics 2019-12-09 Qipin Chen , Wenrui Hao

Application of nonlinearity continuation method to numerical solution of steady-state groundwater flow in variably saturated conditions is presented. In order to solve the system of nonlinear equations obtained by finite volume…

Numerical Analysis · Mathematics 2020-08-04 Denis Anuprienko , Ivan Kapyrin

The Richards equation, a nonlinear elliptic parabolic equation, is widely used to model infiltration in porous media. We develop a finite element method for solving the Richards equation by introducing a new bounded auxiliary variable to…

Numerical Analysis · Mathematics 2025-10-16 Abderrahmane Benfanich , Yves Bourgault , Abdelaziz Beljadid

Flow in variably saturated porous media is typically modelled by the Richards equation, a nonlinear elliptic-parabolic equation which is notoriously challenging to solve numerically. In this paper, we propose a robust and fast iterative…

Numerical Analysis · Mathematics 2023-01-06 Jakob S. Stokke , Koondanibha Mitra , Erlend Storvik , Jakub W. Both , Florin A. Radu

This paper presents a mathematical analysis of a doubly degenerate parabolic equation and its application to the Richards equation using a bounded auxiliary variable. We establish the existence of weak solutions using semi-implicit time…

Analysis of PDEs · Mathematics 2026-04-16 Abderrahmane Benfanich , Yves Bourgault , Abdelaziz Beljadid

The Richards equation is a nonlinear parabolic equation that is commonly used for modelling saturated/unsaturated flow in porous media. We assume that the medium occupies a bounded Lipschitz domain partitioned into two disjoint subdomains…

Numerical Analysis · Mathematics 2018-03-14 David Seus , Koondanibha Mitra , Iuliu Sorin Pop , Florin Adrian Radu , Christian Rohde

Nonlinearity continuation method, applied to boundary value problems for steady-state Richards equation, gradually approaches the solution through a series of intermediate problems. Originally, the Newton method with simple line search…

Numerical Analysis · Mathematics 2021-05-27 Denis Anuprienko

Newton's method for finding an unconstrained minimizer for strictly convex functions, generally speaking, does not converge from any starting point. We introduce and study the damped regularized Newton's method (DRNM). It converges globally…

Optimization and Control · Mathematics 2017-06-27 Roman Polyak

Recently, the nonlinearity continuation method has been used to numerically solve boundary value problems for steady-state Richards equation. The method can be considered as a predictor-corrector procedure with the simplest form which has…

Numerical Analysis · Mathematics 2022-01-17 Denis Anuprienko

This paper focuses on discussing Newton's method and its hybrid with machine learning for the steady state Navier-Stokes Darcy model discretized by mixed element methods. First, a Newton iterative method is introduced for solving the…

Numerical Analysis · Mathematics 2024-03-07 Jianguo Huang , Hui Peng , Haohao Wu

In this work we present an adaptive Newton-type method to solve nonlinear constrained optimization problems in which the constraint is a system of partial differential equations discretized by the finite element method. The adaptive…

Optimization and Control · Mathematics 2017-06-05 Thomas Carraro , Simon Dörsam , Stefan Frei , Daniel Schwarz

This work investigates the application of the Newton's method for the numerical solution of a nonlinear boundary value problem formulated through an ordinary differential equation (ODE). Nonlinear ODEs arise in various mathematical modeling…

We study the numerical approximation of a class of degenerate parabolic stochastic partial differential equations on non-compact metric graphs, which naturally arise in the asymptotic analysis of Hamiltonian flows under small noise…

Numerical Analysis · Mathematics 2026-04-14 Jianbo Cui , Mihály Kovács , Derui Sheng

We show that Newton methods for generalized equations are input-to-state stable with respect to disturbances such as due to inexact computations. We then use this result to obtain convergence and robustness of a multistep Newton-type method…

Optimization and Control · Mathematics 2025-03-18 Torbjørn Cunis , Ilya Kolmanovsky

As a tractable approach, regularization is frequently adopted in sparse optimization. This gives rise to the regularized optimization, aiming at minimizing the $\ell_0$ norm or its continuous surrogates that characterize the sparsity. From…

Optimization and Control · Mathematics 2021-11-17 Shenglong Zhou , Lili Pan , Naihua Xiu

Newton method is one of the most powerful methods for finding solutions of nonlinear equations and for proving their existence. In its "pure" form it has fast convergence near the solution, but small convergence domain. On the other hand…

Optimization and Control · Mathematics 2019-08-27 Boris Polyak , Andrey Tremba

We present an algorithm for the numerical solution of the equations governing combustion in porous inert media. The discretization of the flow problem is performed by the mixed finite element method, the transport problems are discretized…

Numerical Analysis · Mathematics 2016-09-01 Peter Knabner , Gerhard Summ

When combining the numerical concept of variational discretization and semi-smooth Newton methods for the numerical solution of pde constrained optimization with control constraints, special emphasis has to be taken on the implementation,…

Optimization and Control · Mathematics 2009-12-03 Michael Hinze , Morten Vierling

Newton's method is a fundamental technique in optimization with quadratic convergence within a neighborhood around the optimum. However reaching this neighborhood is often slow and dominates the computational costs. We exploit two…

Machine Learning · Computer Science 2016-05-24 Hadi Daneshmand , Aurelien Lucchi , Thomas Hofmann
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