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By using a simple observation that the density processes appearing in Ito's martingale representation theorem are invariant under the change of measures, we establish a non-linear version of the Cameron-Martin formula for solutions of a…

Probability · Mathematics 2010-11-16 G. Liang , A. Lionnet , Z. Qian

Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some non-trivial temporal correlation structure, which renders…

We show that the Schr\"odinger wave functional may be obtained as the product integral of precanonical wave functions on the space of field and space-time variables. The functional derivative Schr\"odinger equation underlying the canonical…

High Energy Physics - Theory · Physics 2017-01-26 I. V. Kanatchikov

The notion of fractional charges was up until now reserved for quasiparticle excitations emerging in strongly correlated quantum systems, such as Laughlin states in the fractional quantum Hall effect, Luttinger quasiparticles, or…

Mesoscale and Nanoscale Physics · Physics 2019-12-25 Roman-Pascal Riwar

In this paper, we consider spectral approximation of fractional differential equations (FDEs). A main ingredient of our approach is to define a new class of generalized Jacobi functions (GJFs), which is intrinsically related to fractional…

Numerical Analysis · Mathematics 2014-08-01 Sheng Chen , Jie Shen , Li-Lian Wang

Machine-learning (ML) based discretization has been developed to simulate complex partial differential equations (PDEs) with tremendous success across various fields. These learned PDE solvers can effectively resolve the underlying solution…

Numerical Analysis · Mathematics 2023-09-12 Yongsheng Chen , Wei Guo , Xinghui Zhong

Gaussian process regression is widely applied in computational science and engineering for surrogate modeling owning to its kernel-based and probabilistic nature. In this work, we propose a Bayesian approach that integrates the variability…

Machine Learning · Computer Science 2025-01-03 Dongwei Ye , Weihao Yan , Christoph Brune , Mengwu Guo

Let $\S$ be a commutative semigroup with identity $e$ and let $\Gamma$ be a compact subset in the pointwise convergence topology of the space $\S'$ of all non-zero multiplicative functions on $\S.$ Given a continuous function $F: \Gamma \to…

Complex Variables · Mathematics 2018-10-24 El Hassan Youssfi

Let $X$ be a ball Banach function space on $\mathbb{R}^n$. In this article, under some mild assumptions about both $X$ and the boundedness of the Hardy--Littlewood maximal operator on both $X$ and the associate space of its convexification,…

Functional Analysis · Mathematics 2023-04-04 Chenfeng Zhu , Dachun Yang , Wen Yuan

This work is devoted to non-linear stochastic Schr\"odinger equations with multiplicative fractional noise, where the stochastic integral is defined following the Riemann-Stieljes approach of Z\"ahle. Under the assumptions that the initial…

Analysis of PDEs · Mathematics 2013-04-01 Olivier Pinaud

A new fractional non-homogeneous counting process has been introduced and developed using the Kilbas and Saigo three-parameter generalization of the Mittag-Leffler function. The probability distribution function of this process reproduces…

Probability · Mathematics 2024-01-01 Nick Laskin

We study solution techniques for parabolic equations with fractional diffusion and Caputo fractional time derivative, the latter being discretized and analyzed in a general Hilbert space setting. The spatial fractional diffusion is realized…

Numerical Analysis · Mathematics 2015-03-05 Ricardo H. Nochetto , Enrique Otarola , Abner J. Salgado

The partial differential equation of Gaussian diffusion is generalized by using the time-fractional derivative of distributed order between 0 and 1, in both the Riemann-Liouville (R-L) and the Caputo (C) sense. For a general distribution of…

Statistical Mechanics · Physics 2008-05-27 Francesco Mainardi , Antonio Mura , Gianni Pagnini , Rudolf Gorenflo

In the spirit of Marcus canonical stochastic differential equations, we study a similar notion of rough differential equations (RDEs), notably dropping the assumption of continuity prevalent in the rough path literature. A new metric is…

Probability · Mathematics 2019-02-12 Ilya Chevyrev , Peter K. Friz

We identify the stochastic processes associated with one-sided fractional partial differential equations on a bounded domain with various boundary conditions. This is essential for modelling using spatial fractional derivatives. We show…

Analysis of PDEs · Mathematics 2017-12-15 Boris Baeumer , Mihály Kovács , Harish Sankaranarayanan

For any real-valued stochastic process $X$ with c\'rdl\'rg paths we define non-empty family of processes which have locally finite total variation, have jumps of the same order as the process $X$ and uniformly approximate its paths on…

Probability · Mathematics 2017-06-26 Rafał M. Łochowski

The calculation of g-functions is essential for the design and simulation of geothermal boreholes. However, existing methods, such as the stacked finite line source (SFLS) model, face challenges regarding computational efficiency and…

Computational Physics · Physics 2025-08-18 Yue Yang , Xiaodong Yang , Chenhui Lin , Luo Xu , Qi Wang , Shuwei Xu , Wenchuan Wu

This paper presents a novel multi-scale method for elliptic partial differential equations with arbitrarily rough coefficients. In the spirit of numerical homogenization, the method constructs problem-adapted ansatz spaces with uniform…

Numerical Analysis · Mathematics 2024-08-05 Philip Freese , Moritz Hauck , Tim Keil , Daniel Peterseim

A new construction of non-Gaussian, rotation-invariant and reflection positive probability measures $\mu$ associated with the $\varphi ^4_3$-model of quantum field theory is presented. Our construction uses a combination of semigroup…

Probability · Mathematics 2025-05-06 Sergio Albeverio , Seiichiro Kusuoka

We provide a new method to approximate a (possibly discontinuous) function using Christoffel-Darboux kernels. Our knowledge about the unknown multivariate function is in terms of finitely many moments of the Young measure supported on the…

Optimization and Control · Mathematics 2021-04-09 Swann Marx , Edouard Pauwels , Tillmann Weisser , Didier Henrion , Jean Lasserre