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Let $M$ be a compact Riemannian homogeneous space (e.g. a Euclidean sphere). We prove existence of a global weak solution of the stochastic wave equation \mathbf D_t\partial_tu=\sum_{k=1}^d\mathbf…

Probability · Mathematics 2016-08-14 Zdzisław Brzeźniak , Martin Ondreját

We prove the existence and uniqueness of weak solution of a Neumann boundary problem for an elliptic partial differential equation (PDE for short) with a singular divergence term which can only be understood in a weak sense. A probabilistic…

Probability · Mathematics 2018-04-24 Xue Yang , Jing Zhang

In this paper we study time-inhomogeneous versions of one-dimensional Stochastic Differential Equations (SDE) involving the Local Time of the unknown process on curves. After proving existence and uniqueness for these SDE under mild…

Probability · Mathematics 2017-09-21 Pierre Etoré , Miguel Martinez

A new method for solving numerically stochastic partial differential equations (SPDEs) with multiple scales is presented. The method combines a spectral method with the heterogeneous multiscale method (HMM) presented in [W. E, D. Liu, and…

Numerical Analysis · Mathematics 2015-05-28 A. Abdulle , G. A. Pavliotis

The existence theory is developed for solutions of the inhomogeneous linearized field equations for causal variational principles. These equations are formulated weakly with an integral operator which is shown to be bounded and symmetric on…

Mathematical Physics · Physics 2022-05-16 Felix Finster , Magdalena Lottner

We derive stability criteria for saddle points of a class of nonsmooth optimization problems in Hilbert spaces arising in PDE-constrained optimization, using metric regularity of infinite-dimensional set-valued mappings. A main ingredient…

Optimization and Control · Mathematics 2017-02-13 Christian Clason , Tuomo Valkonen

In this paper, we address the question of the discretization of Stochastic Partial Differential Equations (SPDE's) for excitable media. Working with SPDE's driven by colored noise, we consider a numerical scheme based on finite differences…

Probability · Mathematics 2014-11-07 Boulakia Muriel , Genadot Alexandre , Thieullen Michèle

In this paper, we study the well-posedness and regularity of non-autonomous stochastic differential algebraic equations (SDAEs) with nonlinear, locally Lipschitz and monotone (2) coefficients of the form (1). The main difficulty is the fact…

Probability · Mathematics 2024-03-18 Oana Silvia Serea , Antoine Tambue , Guy Tsafack

In this note we introduce a new approach to rough and stochastic partial differential equations (RPDEs and SPDEs): we consider general Banach spaces as state spaces and -- for the sake of simiplicity -- finite dimensional sources of noise,…

Probability · Mathematics 2009-08-21 Josef Teichmann

We consider autonomous stochastic ordinary differential equations (SDEs) and weak approximations of their solutions for a general class of sufficiently smooth path-dependent functionals f. Based on tools from functional It\^o calculus, such…

Probability · Mathematics 2016-06-15 Mihály Kovács , Felix Lindner

Methods of Lie group analysis of differential equations are extended to weak solutions of (linear and nonlinear) PDEs, where the term ``weak solution'' comprises the following settings: (a) Distributional solutions. (b) Solutions in…

Functional Analysis · Mathematics 2007-05-23 N. Dapic , M. Kunzinger , S. Pilipovic

The object of the present paper is to find new sufficient conditions for the existence of unique strong solutions to a class of (time-inhomogeneous) stochastic differential equations with random, non-Lipschitzian coefficients. We give an…

Probability · Mathematics 2014-04-04 Guangqiang Lan , Jiang-Lun Wu

A new notion of stochastic transformation is proposed and applied to the study of both weak and strong symmetries of stochastic differential equations (SDEs). The correspondence between an algebra of weak symmetries for a given SDE and an…

Probability · Mathematics 2016-08-02 Francesco C. De Vecchi , Paola Morando , Stefania Ugolini

We consider nonlinear perturbations of the hyperbolic equation in the Hilbert space. Necessary and sufficient conditions for the existence of solutions of boundary-value problem for the corresponding equation and iterative procedures for…

Analysis of PDEs · Mathematics 2023-04-20 Pokutnyi Oleksandr

Backward stochastic partial differential equations of parabolic type in bounded domains are studied in the setting where the coercivity condition is not necessary satisfied and the equation can be degenerate. Some generalized solutions…

Probability · Mathematics 2014-05-26 Nikolai Dokuchaev

We study the Cauchy problem for a semilinear stochastic partial differential equation driven by a finite-dimensional Wiener process. In particular, under the hypothesis that all the coefficients are sufficiently smooth and have bounded…

Analysis of PDEs · Mathematics 2012-02-10 Martina Hofmanova

Inverse problems in scientific computing often require optimization over infinite-dimensional Hilbert spaces. A commonly used solver in such settings is stochastic gradient descent (SGD), where gradients are approximated using randomly…

Optimization and Control · Mathematics 2026-04-14 Sandra Cerrai , Qin Li , Anjali Nair , Jaeyoung Yoon

In this paper, we prove the unique existence and investigate the $L^{p}$-regularity of solutions to stochastic partial differential equations in Hilbert spaces associated with pseudo-differential operators, driven by Hilbert space-valued…

Analysis of PDEs · Mathematics 2025-04-29 Un Cig Ji , Jae Hun Kim

Unique existence of analytically strong solutions to stochastic partial differential equations (SPDE) with drift given by the subdifferential of a quasi-convex function and with general multiplicative noise is proven. The proof applies a…

Probability · Mathematics 2011-04-22 Benjamin Gess

We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data. The proposed approach combines the concepts of stochastic calculus, variational Bayes theory, and sparse learning. We propose the…

Machine Learning · Statistics 2023-06-29 Yogesh Chandrakant Mathpati , Tapas Tripura , Rajdip Nayek , Souvik Chakraborty
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