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In this article, we consider discrete schemes for a fractional diffusion equation involving a tempered fractional derivative in time. We present a semi-discrete scheme by using the local discontinuous Galerkin (LDG) discretization in the…

Numerical Analysis · Mathematics 2017-04-27 Xiaorui Sun , Fengfqun Zhao , Can Li

Existing deterministic variational inference approaches for diffusion processes use simple proposals and target the marginal density of the posterior. We construct the variational process as a controlled version of the prior process and…

Machine Learning · Computer Science 2021-03-02 Christian Wildner , Heinz Koeppl

We have developed a new, very efficient numerical scheme to solve the CR diffusion convection equation that can be applied to the study of the nonlinear time evolution of CR modified shocks for arbitrary spatial diffusion properties. The…

Astrophysics · Physics 2009-11-11 T. W. Jones , H. Kang

This work aims to introduce a heuristic timestep-adaptive algorithm for Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) problems where the flow is dominated by the pressure. In such scenarios, many time-adaptive…

Numerical Analysis · Mathematics 2024-07-02 Ivan Prusak , Davide Torlo , Monica Nonino , Gianluigi Rozza

In this paper, we propose and analyze a time-stepping method for the time fractional Allen-Cahn equation. The key property of the proposed method is its unconditional stability for general meshes, including the graded mesh commonly used for…

Numerical Analysis · Mathematics 2021-04-27 Dianming Hou , Chuanju Xu

Discrete diffusion models have emerged as powerful tools for high-quality data generation. Despite their success in discrete spaces, such as text generation tasks, the acceleration of discrete diffusion models remains under-explored. In…

Machine Learning · Computer Science 2024-12-09 Zixiang Chen , Huizhuo Yuan , Yongqian Li , Yiwen Kou , Junkai Zhang , Quanquan Gu

Computational fluid dynamics (CFD) is a powerful tool for modeling turbulent flow and is commonly used for urban microclimate simulations. However, traditional CFD methods are computationally intensive, requiring substantial hardware…

In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations in order to accelerate the convergence towards the optimal solution. This information is…

Optimization and Control · Mathematics 2021-06-02 Yurii Nesterov , Mihai I. Florea

Finite-difference time-domain (FDTD) simulations often involve physical quantities spanning multiple orders of magnitude, such as the speed of light or electromagnetic field amplitudes. The standard practice for maintaining numerical…

Computational Physics · Physics 2026-03-26 Yannik Mahlau , Lukas Berg , Bodo Rosenhahn

Traditional numerical techniques for solving time-dependent partial-differential-equation (PDE) initial-value problems (IVPs) store a truncated representation of the function values and some number of their time derivatives at each time…

Numerical Analysis · Mathematics 2011-09-08 Hal Finkel

Fractional Fokker-Planck equation plays an important role in describing anomalous dynamics. To the best of our knowledge, the existing discussions mainly focus on this kind of equation involving one diffusion operator. In this paper, we…

Numerical Analysis · Mathematics 2021-09-08 Jing Sun , Weihua Deng , Daxin Nie

This work aims to construct an efficient and highly accurate numerical method to address the time singularity at $t=0$ involved in a class of time-fractional parabolic integro-partial differential equations in one and two dimensions. The…

Numerical Analysis · Mathematics 2024-09-27 Sudarshan Santra , Ratikanta Behera

Particle smoothing methods are used for inference of stochastic processes based on noisy observations. Typically, the estimation of the marginal posterior distribution given all observations is cumbersome and computational intensive. In…

Machine Learning · Computer Science 2017-05-24 H. -Ch. Ruiz , H. J. Kappen

Estimation and inference with modern longitudinal data from wearable devices, which consist of biological signals at high-frequency time points, is burdened by massive computational costs. We propose a distributed estimation and inference…

Methodology · Statistics 2023-09-13 Cole Manschot , Emily C. Hector

This paper is devoted to the numerical analysis of a piecewise constant discontinuous Galerkin method for time fractional subdiffusion problems. The regularity of weak solution is firstly established by using variational approach and…

Numerical Analysis · Mathematics 2022-02-22 Binjie Li , Hao Luo , Xiaoping Xie

In this paper we propose two proximal gradient algorithms for fractional programming problems in real Hilbert spaces, where the numerator is a proper, convex and lower semicontinuous function and the denominator is a smooth function, either…

Optimization and Control · Mathematics 2016-02-01 Radu Ioan Bot , Ernö Robert Csetnek

Diffusion models achieve great success in generating diverse and high-fidelity images, yet their widespread application, especially in real-time scenarios, is hampered by their inherently slow generation speed. The slow generation stems…

Computer Vision and Pattern Recognition · Computer Science 2024-08-19 Shengkun Tang , Yaqing Wang , Caiwen Ding , Yi Liang , Yao Li , Dongkuan Xu

The fact that the first variation of a variational functional must vanish along an extremizer is the base of most effective solution schemes to solve problems of the calculus of variations. We generalize the method to variational problems…

Optimization and Control · Mathematics 2018-02-14 Shakoor Pooseh , Ricardo Almeida , Delfim F. M. Torres

In this paper, we consider a numerical method for the multi-term Caputo-Fabrizio time-fractional diffusion equations (with orders $\alpha_i\in(0,1)$, $i=1,2,\cdots,n$). The proposed method employs a fast finite difference scheme to…

Numerical Analysis · Mathematics 2024-02-22 Bin Fan

Several machine learning applications involve the optimization of higher-order derivatives (e.g., gradients of gradients) during training, which can be expensive in respect to memory and computation even with automatic differentiation. As a…

Machine Learning · Computer Science 2020-11-26 Tianyu Pang , Kun Xu , Chongxuan Li , Yang Song , Stefano Ermon , Jun Zhu
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