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Diffusion-based generative models have achieved promising results recently, but raise an array of open questions in terms of conceptual understanding, theoretical analysis, algorithm improvement and extensions to discrete, structured,…

Machine Learning · Computer Science 2022-09-01 Xingchao Liu , Lemeng Wu , Mao Ye , Qiang Liu

Diffusion-driven flow is a boundary layer flow arising from the interplay of gravity and diffusion in density-stratified fluids when a gravitational field is non-parallel to an impermeable solid boundary. This study investigates…

Fluid Dynamics · Physics 2024-09-23 Lingyun Ding

We present a new method for the approximate solution of the strongly coupled, nonlinear stress-diffusion problem that appears when modeling hydrogen transport in metals. The most salient feature of the proposed approximation is that it is…

Materials Science · Physics 2024-06-21 Eva M. Andrés , Ignacio Romero

We adapt the semiclassical technique, as used in the context of instanton transitions in quantum field theory, to the description of tunneling transmissions at finite energies through potential barriers by complex quantum mechanical…

Quantum Physics · Physics 2007-05-23 G. F. Bonini , A. G. Cohen , C. Rebbi , V. A. Rubakov

Explicit numerical finite difference schemes for partial differential equations are well known to be easy to implement but they are particularly problematic for solving equations whose solutions admit shocks, blowups and discontinuities.…

Numerical Analysis · Mathematics 2016-10-19 Christopher. N. Angstmann , Bruce I. Henry , Byron A. Jacobs , Anna V. McGann

Generating samples from a probability distribution is a fundamental task in machine learning and statistics. This article proposes a novel scheme for sampling from a distribution for which the probability density $\mu({\bf x})$ for ${\bf…

Computation · Statistics 2024-05-22 Hanwen Huang

A parameter estimation problem for a class of semilinear stochastic evolution equations is considered. Conditions for consistency and asymptotic normality are given in terms of growth and continuity properties of the nonlinear part.…

Statistics Theory · Mathematics 2020-02-26 Gregor Pasemann , Wilhelm Stannat

This work is focused on the doubly nonlinear equation, whose solutions represent the bending motion of an extensible, elastic bridge suspended by continuously distributed cables which are flexible and elastic with stiffness k^2. When the…

Mathematical Physics · Physics 2011-02-07 Ivana Bochicchio , Claudio Giorgi , Elena Vuk

Stable Diffusion fine-tuning technique is tried to assist bridge-type innovation. The bridge real photo dataset is built, and Stable Diffusion is fine tuned by using four methods that are Textual Inversion, Dreambooth, Hypernetwork and…

Machine Learning · Computer Science 2024-09-25 Leye Zhang , Xiangxiang Tian , Chengli Zhang , Hongjun Zhang

A recently introduced theoretical framework for modeling the dynamics of X-ray amplified spontaneous emission is based on stochastic sampling of the density matrix of quantum emitters and the radiation field, similarly to other phase-space…

Optics · Physics 2024-06-17 Stasis Chuchurka , Vladislav Sukharnikov , Nina Rohringer

We present a method to downscale idealized geophysical fluid simulations using generative models based on diffusion maps. By analyzing the Fourier spectra of images drawn from different data distributions, we show how one can chain together…

Machine Learning · Computer Science 2023-05-04 Tobias Bischoff , Katherine Deck

The aim of this study is to present a reliable combination of the differential transformation method (DTM) and Pad\'e approximants to make, for the first time, a semi-analytic analysis of the problem of free convection boundary-layer flow…

Numerical Analysis · Mathematics 2012-12-10 Haldun Alpaslan Peker , Galip Oturanc

We consider the construction of semi-implicit linear multistep methods which can be applied to time dependent PDEs where the separation of scales in additive form, typically used in implicit-explicit (IMEX) methods, is not possible. As…

Numerical Analysis · Mathematics 2020-01-14 Giacomo Albi , Lorenzo Pareschi

We generalize the method of obtaining the fundamental linear partial differential equations such as the diffusion and Schrodinger equation, Dirac and telegrapher's equation from a simple stochastic consideration to arrive at certain…

Mathematical Physics · Physics 2008-11-26 Karmadeva Maharana

In this paper, motivated by a problem in stochastic impulse control theory, we aim to study solutions to a free boundary problem of obstacle-type. We obtain sharp estimates for the solution using nonlinear tools which are independent of the…

Analysis of PDEs · Mathematics 2017-02-02 Rohit Jain

We define a new finite element method for a steady state elliptic problem with discontinuous diffusion coefficients where the meshes are not aligned with the interface. We prove optimal error estimates in the $L^2$ norm and $H^1$ weighted…

Numerical Analysis · Mathematics 2016-10-18 Johnny Guzman , Manuel A. Sanchez , Marcus Sarkis

This paper investigates the well-posedness and small-noise asymptotics of a class of stochastic partial differential equations defined on a bounded domain of $\mathbb{R}^d$, where the diffusion coefficient depends nonlinearly and…

Probability · Mathematics 2025-06-23 Sandra Cerrai , Giuseppina Guatteri , Gianmario Tessitore

This article presents a multi-physics methodology for the numerical simulation of physical systems that involve the non-linear interaction of multi-phase reactive fluids and elastoplastic solids, inducing high strain-rates and high…

Computational Physics · Physics 2021-06-04 Tim Wallis , Philip T. Barton , Nikolaos Nikiforakis

In this paper we study the randomized heat equation with homogeneous boundary conditions. The diffusion coeffcient is assumed to be a random variable and the initial condition is treated as a stochastic process. The solution of this…

Probability · Mathematics 2018-02-13 J. Calatayud , J. -C. Cortes , M. Jornet

In this manuscript, we present a novel approach for sampling from a continuous multivariate probability distribution, which may either be explicitly known (up to a normalization factor) or represented via empirical samples. Our method…

Machine Learning · Statistics 2025-03-14 Hamidreza Behjoo , Michael Chertkov