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We propose in this paper efficient first/second-order time-stepping schemes for the evolutional Navier-Stokes-Nernst-Planck-Poisson equations. The proposed schemes are constructed using an auxiliary variable reformulation and sophisticated…

Numerical Analysis · Mathematics 2023-05-17 Xiaolan Zhou , Chuanju Xu

Finite difference method and finite element method are popular methods for solving groundwater flow equations. This paper presents a new method that uses gradually varied functions to solve such equation. In this paper, we have established…

Numerical Analysis · Mathematics 2012-10-17 Li Chen , Xun-Hong Chen

In this paper, we present and analyze a linear fully discrete second order scheme with variable time steps for the phase field crystal equation. More precisely, we construct a linear adaptive time stepping scheme based on the second order…

Numerical Analysis · Mathematics 2023-05-30 Dianming Hou , Zhonghua Qiao

In this paper, we propose and analyze high order efficient schemes for the time fractional Allen-Cahn equation. The proposed schemes are based on the L1 discretization for the time fractional derivative and the extended scalar auxiliary…

Numerical Analysis · Mathematics 2021-03-05 Dianming Hou , Hongyi Zhu , Chuanju Xu

In this paper, we construct and analyze an energy stable scheme by combining the latest developed scalar auxiliary variable (SAV) approach and linear finite element method (FEM) for phase field crystal (PFC) model, and show rigorously that…

Numerical Analysis · Mathematics 2019-10-15 Liupeng Wang , Yunqing Huang , Kai Jiang

In this paper, two efficient and magnetization norm preserving numerical schemes based on the scalar auxiliary variable (SAV) method are developed for calculating the ground state in micromagnetic structures. The first SAV scheme is based…

Numerical Analysis · Mathematics 2025-10-20 Jiayun He , Lei Yang , Jiajun Zhan

In this work, based on the continuous time approach, we propose an accelerated gradient method with adaptive residual restart for convex multiobjective optimization problems. For the first, we derive rigorously the continuous limit of the…

Optimization and Control · Mathematics 2025-02-06 Hao Luo , Liping Tang , Xinmin Yang

Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework. Most proposed flow models therefore either restrict to a function…

Machine Learning · Computer Science 2021-06-10 T. Anderson Keller , Jorn W. T. Peters , Priyank Jaini , Emiel Hoogeboom , Patrick Forré , Max Welling

We develop and analyze a highly efficient, second-order time-marching scheme for infinite-dimensional nonlinear geophysical fluid models, designed to accurately approximate invariant measures-that is, the stationary statistical properties…

Numerical Analysis · Mathematics 2025-10-08 Daozhi Han , Xiaoming Wang

Lax-Wendroff flux reconstruction (LWFR) schemes have high order of accuracy in both space and time despite having a single internal time step. Here, we design a Jacobian-free LWFR type scheme to solve the special relativistic hydrodynamics…

Numerical Analysis · Mathematics 2025-02-05 Sujoy Basak , Arpit Babbar , Harish Kumar , Praveen Chandrashekar

We have developed an efficient and unconditionally energy-stable method for simulating droplet formation dynamics. Our approach involves a novel time-marching scheme based on the scalar auxiliary variable technique, specifically designed…

Numerical Analysis · Mathematics 2024-02-08 Jinpeng Zhang , Changjuan Zhang , Xiaoping Wang

Implicit-explicit (IMEX) time integration schemes are well suited for nonlinear structural dynamics because of their low computational cost and high accuracy. However, stability of IMEX schemes cannot be guaranteed for general nonlinear…

Numerical Analysis · Mathematics 2025-06-27 Sun-Beom Kwon , Arun Prakash

The Stein Variational Gradient Descent (SVGD) algorithm is a deterministic particle method for sampling. However, a mean-field analysis reveals that the gradient flow corresponding to the SVGD algorithm (i.e., the Stein Variational Gradient…

Machine Learning · Statistics 2024-05-10 Ye He , Krishnakumar Balasubramanian , Bharath K. Sriperumbudur , Jianfeng Lu

A necessary and sufficient condition for linear stability of inviscid parallel shear flow is formulated by a novel variational method, where the velocity profile is assumed to be monotonic and analytic. Unstable eigenvalues of the Rayleigh…

Fluid Dynamics · Physics 2013-09-03 Makoto Hirota , Philip J. Morrison , Yuji Hattori

Studies on generalization performance of machine learning algorithms under the scope of information theory suggest that compressed representations can guarantee good generalization, inspiring many compression-based regularization methods.…

Machine Learning · Computer Science 2019-10-16 Antoine Saporta , Yifu Chen , Michael Blot , Matthieu Cord

We construct high-order semi-discrete-in-time and fully discrete (with Fourier-Galerkin in space) schemes for the incompressible Navier-Stokes equations with periodic boundary conditions, and carry out corresponding error analysis. The…

Numerical Analysis · Mathematics 2021-03-23 Fukeng Huang , Jie Shen

This article presents stability and convergence analyses of subgrid multiscale stabilized finite element formulation of non-Newtonian power-law fluid flow model strongly coupled with variable coefficients Advection-Diffusion-Reaction…

Numerical Analysis · Mathematics 2021-02-19 Manisha Chowdhury , B. V. Rathish Kumar

An efficient numerical scheme based on the scalar auxiliary variable (SAV) and marker and cell scheme (MAC) is constructed for the Navier-Stokes equations. A particular feature of the scheme is that the nonlinear term is treated explicitly…

Numerical Analysis · Mathematics 2019-09-12 Xiaoli Li , Jie Shen

In this study, we investigate the performance of two novel first-order optimization algorithms, namely the rescaled-gradient flow (RGF) and the signed-gradient flow (SGF). These algorithms are derived from the forward Euler discretization…

Machine Learning · Computer Science 2025-03-19 Siqi Zhang , Mouhacine Benosman , Orlando Romero

We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled gradient descent methods and is theoretically grounded in…

Machine Learning · Computer Science 2023-05-16 Rafael Orozco , Ali Siahkoohi , Mathias Louboutin , Felix J. Herrmann