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Physics Informed Neural Networks is a numerical method which uses neural networks to approximate solutions of partial differential equations. It has received a lot of attention and is currently used in numerous physical and engineering…

Numerical Analysis · Mathematics 2025-07-10 Dimitrios Gazoulis , Ioannis Gkanis , Charalambos G. Makridakis

We investigate a class of elliptic and parabolic partial differential equations driven by p(u) laplacian. This dependence necessitates the use of variable exponent Sobolev spaces specifically tailored to the anisotropic framework. For the…

Analysis of PDEs · Mathematics 2025-10-17 Kaushik Bal , Shilpa Gupta

This paper extends deterministic notions of Strong Stability Preservation (SSP) to the stochastic setting, enabling nonlinearly stable numerical solutions to stochastic differential equations (SDEs) and stochastic partial differential…

Numerical Analysis · Mathematics 2024-12-10 James Woodfield

In this article we present a system of coupled non-linear PDEs modeling an anisotropic electrokinetic flow. We show the existence of suitable weak solutions in three spatial dimensions, that is weak solutions which fulfill an energy…

Analysis of PDEs · Mathematics 2024-07-30 Dietmar Hömberg , Robert Lasarzik , Luisa Plato

Thermodynamically consistent models for two-phase flow in porous media have attracted significant attention in recent years. In this paper, we prove the existence, uniqueness and regularity of the weak solution to such a recent model…

Analysis of PDEs · Mathematics 2026-02-05 Huangxin Chen , Jisheng Kou , Haitao Leng , Shuyu Sun , Hai Zhao

Motivated by applications to fluid dynamics, we study rough differential equations (RDEs) and rough partial differential equations (RPDEs) with non-Lipschitz drifts. We prove well-posedness and existence of a flow for RDEs with Osgood…

Analysis of PDEs · Mathematics 2025-02-18 Lucio Galeati , James-Michael Leahy , Torstein Nilssen

In this paper, we discuss energy estimates for a particular class of linear hyperbolic boundary value problems known as weakly regular of real type. Such class, also called WR in the literature, is relevant in many physical situations like…

Analysis of PDEs · Mathematics 2023-10-10 Santiago Correa

The aim of this paper is to prove global in time existence of weak solutions for a viscoelastic phase separation. We consider the case with singular potentials and degenerate mobilities. Our model couples the diffusive interface model with…

Analysis of PDEs · Mathematics 2022-08-31 Aaron Brunk , Maria Lukacova-Medvidova

A method for finding exact solutions of nonlinear differential equations is presented. Our method is based on the application of the Newton polygons corresponding to nonlinear differential equations. It allows one to express exact solutions…

Exactly Solvable and Integrable Systems · Physics 2007-05-23 Nikolai A. Kudryashov , Maria V. Demina

We consider the weighted parabolic problem of the type \begin{equation*} \begin{split} \left\{\begin{array}{ll} u_t-\mathrm{div}(\omega_2(x)|\nabla u|^{p-2} \nabla u )= \lambda \omega_1(x) |u|^{p-2}u,& x\in\Omega, u(x,0)=f(x),& x\in\Omega,…

Analysis of PDEs · Mathematics 2019-05-14 Iwona Chlebicka , Anna Zatorska-Goldstein

Here we study a nonlinear thermoelasticity hyperbolic-parabolic system describing the balance of momentum and internal energy of a heat-conducting elastic body, preserving the positivity of temperature. So far, no global existence results…

Analysis of PDEs · Mathematics 2023-12-20 Tomasz Cieślak , Boris Muha , Srđan Trifunović

We investigate existence, uniqueness and regularity for solutions of rough parabolic equations of the form $\partial _tu-A_tu-f=(\dot X_t(x) \cdot \nabla + \dot Y_t(x))u$ on $[0,T]\times\mathbb{R}^d.$ To do so, we introduce a concept of…

Probability · Mathematics 2020-07-09 Antoine Hocquet , Torstein Nilssen

We prove global existence for solutions arising from small initial data for a large class of quasilinear wave equations satisfying the `weak null condition' of Lindblad and Rodnianski, significantly enlarging upon the class of equations for…

Analysis of PDEs · Mathematics 2018-10-02 Joseph Keir

Based on Dou Huashu's energy gradient theory, this paper focuses on the weak singularity of the incompressible Navier-Stokes (NS) equations in steady, fully developed flows. When the gradient of total mechanical energy is perpendicular to…

Fluid Dynamics · Physics 2026-03-10 Chio Chon Kit

In this paper we propose a notion of viscosity solutions for path dependent semi-linear parabolic PDEs. This can also be viewed as viscosity solutions of non-Markovian backward SDEs, and thus extends the well-known nonlinear Feynman-Kac…

Analysis of PDEs · Mathematics 2014-01-15 Ibrahim Ekren , Christian Keller , Nizar Touzi , Jianfeng Zhang

We establish a new type of weak Harnack estimates with optimal parabolic tail for the weak supersolutions to a doubly nonlinear nonlocal $p$-Laplace equation, which is modeled on the nonlocal Trudinger equation. Our results are achieved by…

Analysis of PDEs · Mathematics 2025-02-28 Bin Shang , Chao Zhang

In this article, we investigate the existence and properties of time-periodic solutions for damped evolutionary partial differential equations subject to periodic forcing. Particular emphasis is placed on configurations where the energy…

Analysis of PDEs · Mathematics 2026-05-19 Camille Laurent , Ivonne Rivas

This paper aims to employ the weak Galerkin method to solve a class of nonlinear eigenvalue problems. We proved the weak Galerkin scheme produces lower bound for the energy. Moreover, by the post-processing technique, we obtain lower bound…

Numerical Analysis · Mathematics 2026-05-29 Wei Lu , Qilong Zhai

Our paper deals with three-dimensional nonsteady Navier-Stokes equations for non-Newtonian compressible fluids. It contains a~derivation of the relative energy inequality for the weak solutions to these equations. We show that the standard…

Analysis of PDEs · Mathematics 2022-10-25 Richard Andrášik , Václav Mácha , Rostislav Vodák

Physics informed neural networks (PINNs) have emerged as a powerful tool to provide robust and accurate approximations of solutions to partial differential equations (PDEs). However, PINNs face serious difficulties and challenges when…

Machine Learning · Computer Science 2023-07-11 Rajat Arora