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We consider scaled Brownian motion (sBm), a random process described by a diffusion equation with explicitly time-dependent diffusion coefficient $D(t) = D_0 t^{\alpha - 1}$ (Batchelor's equation) which, for $\alpha < 1$, is often used for…

Data Analysis, Statistics and Probability · Physics 2015-06-17 Felix Thiel , Igor M. Sokolov

We propose a new numerical method for one dimensional stochastic differential equations (SDEs). The main idea of this method is based on a representation of a weak solution of a SDE with a time changed Brownian motion, dated back to Doeblin…

Probability · Mathematics 2020-06-05 Masaaki Fukasawa , Mitsumasa Ikeda

In this thesis, we extend the recently introduced theory of stochastic modified equations (SMEs) for stochastic gradient optimization algorithms. In Ch. 3 we study time-inhomogeneous SDEs driven by Brownian motion. For certain SDEs we prove…

Probability · Mathematics 2025-11-26 Stefan Perko

The scaled Brownian motion (SBM) is regarded as one of the paradigmatic random processes, featuring the anomalous diffusion property characterized by the diffusion exponent. It is a Gaussian, self-similar process with independent…

Probability · Mathematics 2024-04-29 Hubert Woszczek , Aleksei Chechkin , Agnieszka Wylomanska

In this paper, we introduce the concept of Developmental Partial Differential Equation (DPDE), which consists of a Partial Differential Equation (PDE) on a time-varying manifold with complete coupling between the PDE and the manifold's…

Optimization and Control · Mathematics 2015-09-23 Nastassia Pouradier Duteil , Francesco Rossi , Ugo Boscain , Benedetto Piccoli

We study a second-order parabolic equation with divergence form elliptic operator, having piecewise constant diffusion coefficients with two points of discontinuity. Such partial differential equations appear in the modelization of…

Probability · Mathematics 2013-12-31 Zhen-Qing Chen , Mounir Zili

In this work, we prove a version of H\"{o}rmander's theorem for a stochastic evolution equation driven by a trace-class fractional Brownian motion with Hurst exponent $\frac{1}{2} < H < 1$ and an analytic semigroup on a given separable…

Probability · Mathematics 2020-03-19 Jorge A. de Nascimento , Alberto Ohashi

We study solution techniques for an evolution equation involving second order derivative in time and the spectral fractional powers, of order $s \in (0,1)$, of symmetric, coercive, linear, elliptic, second-order operators in bounded domains…

Numerical Analysis · Mathematics 2018-06-18 Lehel Banjai , Enrique Otarola

This paper is concerned with a strongly degenerate convection-diffusion equation in one space dimension whose convective flux involves a non-linear function of the total mass to one side of the given position. This equation can be…

Numerical Analysis · Mathematics 2010-07-12 Fernando Betancourt , Raimund Bürger , Kenneth H. Karlsen

Anomalous diffusion is frequently described by scaled Brownian motion (SBM), a Gaussian process with a power-law time dependent diffusion coefficient. Its mean squared displacement is $\langle x^2(t)\rangle\simeq\mathscr{K}(t)t$ with…

Statistical Mechanics · Physics 2014-12-24 J. -H. Jeon , A. V. Chechkin , R. Metzler

In this paper we consider multi-dimensional partial differential equations of parabolic type involving divergence form operators that possess a discontinuous coefficient matrix along some smooth interface. The solution of the equation is…

Probability · Mathematics 2020-03-27 Pierre Etore , Miguel Martinez

The Dyson Brownian Motion (DBM) describes the stochastic evolution of $N$ points on the line driven by an applied potential, a Coulombic repulsion and identical, independent Brownian forcing at each point. We use an explicit tamed Euler…

Numerical Analysis · Mathematics 2015-06-16 Xingjie Helen Li , Govind Menon

Probabilistic ordinary differential equation (ODE) solvers have been introduced over the past decade as uncertainty-aware numerical integrators. They typically proceed by assuming a functional prior to the ODE solution, which is then…

Numerical Analysis · Mathematics 2025-03-25 Yvann Le Fay , Simo Särkkä , Adrien Corenflos

In this paper, the method of constructing the asymptotics of the fundamental solution of the Cauchy problem for a degenerate linear parabolic equation with small diffusion is considered. Based on the results obtained in \cite{dn}, the study…

Analysis of PDEs · Mathematics 2020-09-17 Mark Rakhel

A one-way wave equation is an evolution equation in one of the space directions that describes (approximately) a wave field. The exact wave field is approximated in a high frequency, microlocal sense. Here we derive the pseudodifferential…

Analysis of PDEs · Mathematics 2007-05-23 Christiaan C. Stolk

We investigate the large-scale behaviour of the Self-Repelling Brownian Polymer (SRBP) in the critical dimension $d=2$. The SRBP is a model of self-repelling motion, which is formally given by the solution a stochastic differential equation…

Probability · Mathematics 2024-03-12 Giuseppe Cannizzaro , Harry Giles

We consider divergence form uniformly parabolic SPDEs with bounded and measurable leading coefficients and possibly growing lower-order coefficients in the deterministic part of the equations. We look for solutions which are summable to the…

Probability · Mathematics 2009-08-13 N. V. Krylov

We study a voting model on a branching Brownian motion process on $\mathbb{R}$ in which the diffusivity of each child particle is increased from that of the parent by a factor of $\gamma>1$. The probability distribution of the overall vote…

Analysis of PDEs · Mathematics 2023-12-29 Alexander Dunlap , Lenya Ryzhik

In this paper, we introduce a new nonlinear evolution partial differential equation for sparse deconvolution problems. The proposed PDE has the form of continuity equation that arises in various research areas, e.g. fluid dynamics and…

Optimization and Control · Mathematics 2011-04-04 Yu Mao , Bin Dong , Stanley Osher

Solving high-dimensional parabolic partial differential equations (PDEs) with deep learning methods is often computationally and memory intensive, primarily due to the need for automatic differentiation (AD) to compute large Hessian…

Numerical Analysis · Mathematics 2026-01-13 Wei Cai , Shuixin Fang , Tao Zhou
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