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In this work we propose a nonlinear stabilization technique for convection-diffusion-reaction and pure transport problems discretized with space-time isogeometric analysis. The stabilization is based on a graph-theoretic artificial…

Numerical Analysis · Computer Science 2019-11-18 Jesús Bonilla , Santiago Badia

In this paper, we introduce a novel high-order shock tracking method and provide a proof of concept. Our method leverages concepts from implicit shock tracking and extended discontinuous Galerkin methods, primarily designed for solving…

Numerical Analysis · Mathematics 2023-11-29 Jakob Vandergrift , Florian Kummer

This paper analyzes a time-stepping discontinuous Galerkin method for modified anomalous subdiffusion problems with two time fractional derivatives of orders $ \alpha $ and $ \beta $ ($ 0 < \alpha < \beta < 1 $). The stability of this…

Numerical Analysis · Mathematics 2017-11-16 Binjie Li , Hao Luo , Xiaoping Xie

We devise a Hybrid High-Order (HHO) method for the coupling between the acoustic and elastic wave equations in the time domain. A first-order formulation in time is considered. The HHO method can use equal-order and mixed-order settings…

Numerical Analysis · Mathematics 2025-06-23 Romain Mottier , Alexandre Ern , Rekha Khot , Laurent Guillot

Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive…

Machine Learning · Computer Science 2018-06-21 Celestine Dünner , Aurelien Lucchi , Matilde Gargiani , An Bian , Thomas Hofmann , Martin Jaggi

Recently, a new class of BDF schemes proposed in [F. Huang and J. Shen, SIAM J Numer. Anal., 62.4, 1609--1637] for the parabolic type equations are studied in this paper. The basic idea is based on the Taylor expansions at time…

Numerical Analysis · Mathematics 2025-07-10 Xiaoyi Li , Aijie Cheng , Zhengguang Liu

Among the single-trajectory Gaussian-based methods for solving the time-dependent Schr\"{o}dinger equation, the variational Gaussian approximation is the most accurate one. In contrast to Heller's original thawed Gaussian approximation, it…

Quantum Physics · Physics 2024-09-26 Roya Moghaddasi Fereidani , Jiří J. L. Vaníček

Motivated by the problem of solving the Einstein equations, we discuss high order finite difference discretizations of first order in time, second order in space hyperbolic systems.Particular attention is paid to the case when first order…

General Relativity and Quantum Cosmology · Physics 2010-01-18 M. Chirvasa , S. Husa

We study solutions to nonlinear hyperbolic systems with fully nonlinear relaxation terms in the limit of, both, infinitely stiff relaxation and arbitrary late time. In this limit, the dynamics is governed by effective systems of parabolic…

Analysis of PDEs · Mathematics 2012-10-18 Sebastiano Boscarino , Philippe G. LeFloch , Giovanni Russo

Time-fractional parabolic equations with a Caputo time derivative are considered. For such equations, we explore and further develop the new methodology of the a-posteriori error estimation and adaptive time stepping proposed in [7]. We…

Numerical Analysis · Mathematics 2023-01-27 Sebastian Franz , Natalia Kopteva

The high cost of sequential time integration is one major constraint that limits the speedup of a time-parallel algorithm like the Parareal algorithm due to the difficulty of coarsening time steps in a stiff numerical problem. To address…

Computational Physics · Physics 2025-03-06 Weifan Liu

We propose a novel class of temporal high-order parametric finite element methods for solving a wide range of geometric flows of curves and surfaces. By incorporating the backward differentiation formulae (BDF) for time discretization into…

Numerical Analysis · Mathematics 2024-08-21 Wei Jiang , Chunmei Su , Ganghui Zhang

Network interactions between dynamical units are often subject to time delay. We develop a phase reduction method for delay-coupled oscillator networks. The method is based on rewriting the delay-differential equation as an ordinary…

Dynamical Systems · Mathematics 2025-11-03 Christian Bick , Bob W. Rink , Babette A. J. de Wolff

Currently existing energy-stable parametric finite element methods for surface diffusion flow and other flows are usually limited to first-order accuracy in time. Designing a high-order algorithm for geometric flows that can also be…

Numerical Analysis · Mathematics 2024-07-15 Meng Li , Yihang Guo , Jingjiang Bi

When evolving in time the solution of a hyperbolic partial differential equation, it is often desirable to use high order strong stability preserving (SSP) time discretizations. These time discretizations preserve the monotonicity…

Numerical Analysis · Mathematics 2017-08-02 Sidafa Conde , Sigal Gottlieb , Zachary J. Grant , John N. Shadid

We propose and analyse new stabilized time marching schemes for Phase Fields model such as Allen-Cahn and Cahn-Hillard equations, when discretized in space with high order finite differences compact schemes. The stabilization applies to…

Numerical Analysis · Mathematics 2019-10-01 Matthieu Brachet , Jean-Paul Chehab

We introduce a class of unconditionally energy stable, high order accurate schemes for gradient flows in a very general setting. The new schemes are a high order analogue of the minimizing movements approach for generating a time discrete…

Numerical Analysis · Mathematics 2020-02-11 Alexander Zaitzeff , Selim Esedoglu , Krishna Garikipati

In this paper, we establish an initial theory regarding the Second Order Asymptotical Regularization (SOAR) method for the stable approximate solution of ill-posed linear operator equations in Hilbert spaces, which are models for linear…

Numerical Analysis · Mathematics 2018-08-28 Ye Zhang , Bernd Hofmann

We develop a high-order, explicit method for acoustic scattering in three space dimensions based on a combined-field time-domain integral equation. The spatial discretization, of Nystr\"om type, uses Gaussian quadrature on panels combined…

Numerical Analysis · Mathematics 2020-01-29 Alex H. Barnett , Leslie Greengard , Tom Hagstrom

First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity. Second-order methods, while able to provide faster convergence, have been much less explored…

Machine Learning · Statistics 2017-12-01 Naman Agarwal , Brian Bullins , Elad Hazan
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