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A novel algorithm for the direct numerical simulation of the variable-density, low-Mach Navier-Stokes equations extending the method of Kim, Moin, and Moser (1987) for incompressible flow is presented here. A Fourier representation is…

Fluid Dynamics · Physics 2022-06-22 Bryan W. Reuter , Todd A. Oliver , Robert D. Moser

This report presents a low computational and cognitive complexity, stable, time accurate and adaptive method for the Navier-Stokes equations. The improved method requires a minimally intrusive modification to an existing program based on…

Numerical Analysis · Mathematics 2019-02-01 Victor DeCaria , William Layton , Haiyun Zhao

A posteriori error estimates are an important tool to bound discretization errors in terms of computable quantities avoiding regularity conditions that are often difficult to establish. For non-linear and non-differentiable problems,…

Numerical Analysis · Mathematics 2024-06-12 Sören Bartels , Alex Kaltenbach

Both discrete and continuum models have been widely used to study rapid granular flow, discrete model is accurate but computationally expensive, whereas continuum model is computationally efficient but its accuracy is doubtful in many…

Fluid Dynamics · Physics 2015-12-24 Xizhong Chen , Junwu Wang , Jinghai Li

We introduce in this paper the numerical analysis of high order both in time and space Lagrange-Galerkin methods for the conservative formulation of the advection-diffusion equation. As time discretization scheme we consider the Backward…

Numerical Analysis · Mathematics 2024-01-05 Rodolfo Bermejo , Manuel Colera

We derive a reliable a posteriori error estimate for a cell-centered finite volume scheme approximating a cross-diffusion system modeling ion transport through nanopores. To this end, we derive a stability framework that is independent of…

Numerical Analysis · Mathematics 2025-07-28 Arne Berrens , Jan Giesselmann

We develop a novel fast iterative moment method for the steady-state simulation of near-continuum flows, which are modeled by the high-order moment system derived from the Boltzmann-BGK equation. The fast convergence of the present method…

Numerical Analysis · Mathematics 2025-07-30 Guanghan Li , Chunwu Wang , Zhicheng Hu

In this paper, we study the "a posteriori" error estimate corresponding to the Brinkman-Darcy-Forchheimer problem. We introduce the variational formulation discretised by using the finite element method. Then, we establish an "a posteriori"…

Numerical Analysis · Mathematics 2021-04-29 Toni Sayah

In this paper, we study numerical methods for the homogenization of linear second-order elliptic equations in nondivergence-form with periodic diffusion coefficients and large drift terms. Upon noting that the effective diffusion matrix can…

Numerical Analysis · Mathematics 2025-06-18 Timo Sprekeler , Han Wu , Zhiwen Zhang

Port-Hamiltonian systems provide an energy-based modeling paradigm for dynamical input-state-output systems. At their core, they fulfill an energy balance relating stored, dissipated and supplied energy. To accurately resolve this energy…

Numerical Analysis · Mathematics 2024-12-17 Andreas Bartel , Manuel Schaller

We present a method to estimate the segregation parameter, $S,$ a key input in a continuum transport model of particulate flows. $S$ is determined by minimizing the difference between measured and model-predicted concentration profiles. To…

We consider Poisson's equation with a finite number of weighted Dirac masses as a source term, together with its discretization by means of conforming finite elements. For the error in fractional Sobolev spaces, we propose residual-type a…

Numerical Analysis · Mathematics 2015-07-30 Fernando D. Gaspoz , Pedro Morin , Andreas Veeser

We present a conditional diffusion model - ConDiSim, for simulation-based inference of complex systems with intractable likelihoods. ConDiSim leverages denoising diffusion probabilistic models to approximate posterior distributions,…

Machine Learning · Computer Science 2025-10-17 Mayank Nautiyal , Andreas Hellander , Prashant Singh

A posteriori estimates give bounds on the error between the unknown solution of a partial differential equation and its numerical approximation. We present here the methodology based on H1-conforming potential and H(div)-conforming…

Numerical Analysis · Mathematics 2025-05-30 Martin Vohralík , Soleiman Yousef

A posteriori error analysis is a technique to quantify the error in particular simulations of a numerical approximation method. In this article, we use such an approach to analyze how various error components propagate in certain moving…

Numerical Analysis · Mathematics 2019-09-04 Jay A. Stotsky , David M. Bortz

This work develops Monte Carlo Euler adaptive time stepping methods for the weak approximation problem of jump diffusion driven stochastic differential equations. The main result is the derivation of a new expansion for the omputational…

Numerical Analysis · Mathematics 2007-05-23 E. Mordecki , A. Szepessy , R. Tempone , G. E. Zouraris

In this work, we propose a residual-based a posteriori error estimator for algebraic flux-corrected (AFC) schemes for stationary convection-diffusion equations. A global upper bound is derived for the error in the energy norm for a general…

Numerical Analysis · Mathematics 2024-01-15 Abhinav Jha

We present a numerical method to deal efficiently with large numbers of particles in incompressible fluids. The interactions between particles and fluid are taken into account by a physically motivated ansatz based on locally defined drag…

Condensed Matter · Physics 2016-08-31 W. Kalthoff , S. Schwarzer , G. H. Ristow , H. J. Herrmann

In this paper, we propose stochastic structure-preserving schemes to compute the effective diffusivity for particles moving in random flows. We first introduce the motion of particles using the Lagrangian formulation, which is modeled by…

Numerical Analysis · Mathematics 2020-08-24 Junlong Lyu , Zhongjian Wang , Jack Xin , Zhiwen Zhang

This paper is concerned with probabilistic techniques for forecasting dynamical systems described by partial differential equations (such as, for example, the Navier-Stokes equations). In particular, it is investigating and comparing…

Machine Learning · Computer Science 2025-11-07 Hans Harder , Abhijeet Vishwasrao , Luca Guastoni , Ricardo Vinuesa , Sebastian Peitz