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A method to calculate the adjoint solution for a large class of partial differential equations is discussed. It differs from the known continuous and discrete adjoint, including automatic differentiation. Thus, it represents an alternative,…

Numerical Analysis · Mathematics 2018-05-08 Julius Reiss , Mathias Lemke , Jörn Sesterhenn

We develop a framework for non-asymptotic analysis of deterministic samplers used for diffusion generative modeling. Several recent works have analyzed stochastic samplers using tools like Girsanov's theorem and a chain rule variant of the…

Machine Learning · Computer Science 2023-03-07 Sitan Chen , Giannis Daras , Alexandros G. Dimakis

We study the analytic properties of tree-level wavefunction coefficients in quasi-de Sitter space. We focus on theories which spontaneously break dS boost symmetries and can produce significant non-Gaussianities. The corresponding…

High Energy Physics - Theory · Physics 2021-12-15 David Meltzer

A computationally efficient method for solving three-dimensional, viscous, incompressible flows on unbounded domains is presented. The method formally discretizes the incompressible Navier-Stokes equations on an unbounded staggered…

Fluid Dynamics · Physics 2016-05-25 Sebastian Liska , Tim Colonius

It is shown that, by imposing reparametrization invariance, one may derive a variety of stochastic equations describing the dynamics of surface growth and identify the physical processes responsible for the various terms. This approach…

Condensed Matter · Physics 2009-10-28 M. Marsili , A. Maritan , F. Toigo , J. R. Banavar

A new method is proposed to numerically extract the diffusivity of a (typically nonlinear) diffusion equation from underlying stochastic particle systems. The proposed strategy requires the system to be in local equilibrium and have…

Statistical Mechanics · Physics 2018-05-09 Peter Embacher , Nicolas Dirr , Johannes Zimmer , Celia Reina

This paper addresses the challenging numerical simulation of nonlinear hybrid stochastic functional differential equations with infinite delays. We first propose an explicit scheme using space and time truncation, requiring only finite…

Numerical Analysis · Mathematics 2025-12-23 Guozhen Li , Xiaoyue Li , Xuerong Mao

Active Flux is a modified Finite Volume method that evolves additional Degrees of Freedom for each cell that are located on the interface by a non-conservative method to compute high-order approximations to the numerical fluxes through the…

Numerical Analysis · Mathematics 2024-12-10 Lukas Hensel , Gudrun Grünwald , Katharina Kormann , Rainer Grauer

Multiphase flows are commonly found in chemical engineering processes such as distillation columns, bubble columns, fluidized beds and heat exchangers. The physical boundaries of domains in numerical simulations of multiphase flows are…

Fluid Dynamics · Physics 2023-04-21 Tanyakarn Treeratanaphitak , Nasser Mohieddin Abukhdeir

We study a model describing the slow flow of a fluid through a deformable, porous, elastic solid undergoing small deformations. The stress-strain relationship of the solid incorporates nonlinear effects, formulated as a perturbation of the…

Numerical Analysis · Mathematics 2026-04-28 Andrea Bonito , Vivette Girault , Diane Guignard

The present work proposes a well-balanced finite volume-type numerical method for the solution of non-conservative hyperbolic partial differential equations (PDEs) with source terms. The method is characterized, first, by the use of a…

Numerical Analysis · Mathematics 2026-05-06 Chiara Colombo , Caterina Dalmaso , Lucas O. Müller , Annunziato Siviglia

In this paper, we propose a new adaptation of the D-iteration algorithm to numerically solve the differential equations. This problem can be reinterpreted in 2D or 3D (or higher dimensions) as a limit of a diffusion process where the…

Numerical Analysis · Computer Science 2012-04-30 Dohy Hong

This article is devoted to deduce the expression of the Green's function related to a general constant coefficients fractional difference equation coupled to Dirichlet conditions. In this case, due to the points where some of the fractional…

Classical Analysis and ODEs · Mathematics 2022-12-20 Alberto Cabada , Nikolay D. Dimitrov , Jagan Mohan Jonnalagadda

In this paper, we propose an algorithmic framework, dubbed inertial alternating direction methods of multipliers (iADMM), for solving a class of nonconvex nonsmooth multiblock composite optimization problems with linear constraints. Our…

Optimization and Control · Mathematics 2023-01-26 Le Thi Khanh Hien , Duy Nhat Phan , Nicolas Gillis

We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation…

Machine Learning · Statistics 2017-02-21 Andrew C. Miller , Nicholas Foti , Ryan P. Adams

This paper considers a class of structured fractional minimization problems. The numerator consists of a differentiable function, a simple nonconvex nonsmooth function, a concave nonsmooth function, and a convex nonsmooth function composed…

Optimization and Control · Mathematics 2025-04-01 Ganzhao Yuan

An improved scheme for computing coupling parameters of the Kardar-Parisi-Zhang equation from a collection of successive interface profiles, is presented. The approach hinges on a spectral representation of this equation. An appropriate…

Statistical Mechanics · Physics 2009-10-31 Achille Giacometti , Maurice Rossi

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

High dimensional data reduction techniques are provided by using partial least squares within deep learning. Our framework provides a nonlinear extension of PLS together with a disciplined approach to feature selection and architecture…

Methodology · Statistics 2021-06-29 Nicholas Polson , Vadim Sokolov , Jianeng Xu

Learning probabilistic surrogates for partial differential equations remains challenging in data-scarce regimes: neural operators require large amounts of high-fidelity data, while generative approaches typically sacrifice resolution…

Computation · Statistics 2025-12-18 Sahil Bhola , Karthik Duraisamy