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Discovering constants of motion is meaningful in helping understand the dynamical systems, but inevitably needs proficient mathematical skills and keen analytical capabilities. With the prevalence of deep learning, methods employing neural…

Machine Learning · Computer Science 2025-04-15 Wenqi Fang , Chao Chen , Yongkui Yang , Zheng Wang

Many applications of computational fluid dynamics require multiple simulations of a flow under different input conditions. In this paper, a numerical algorithm is developed to efficiently determine a set of such simulations in which the…

Numerical Analysis · Mathematics 2017-05-29 Max Gunzburger , Nan Jiang , Zhu Wang

Consistency models imitate the multi-step sampling of score-based diffusion in a single forward pass of a neural network. They can be learned in two ways: consistency distillation and consistency training. The former relies on the true…

Machine Learning · Computer Science 2025-07-03 Thibaut Issenhuth , Sangchul Lee , Ludovic Dos Santos , Jean-Yves Franceschi , Chansoo Kim , Alain Rakotomamonjy

Second-order dynamical systems are important tools for solving optimization problems, and most of existing works in this field have focused on unconstrained optimization problems. In this paper, we propose an inertial primal-dual dynamical…

Optimization and Control · Mathematics 2022-05-23 Xin He , Rong Hu , Ya-Ping Fang

We present a simplified version of the threshold dynamics algorithm given in the work of Esedoglu and Otto (2015). The new version still allows specifying N-choose-2 possibly distinct surface tensions and N-choose-2 possibly distinct…

Numerical Analysis · Mathematics 2018-07-19 Tiago Salvador , Selim Esedoglu

Transformers are designed for discrete tokens, yet many real-world signals are continuous processes observed through noisy sampling. Discrete tokenizations (raw values, patches, finite differences) can be brittle in low signal-to-noise…

Machine Learning · Computer Science 2026-01-21 Griffin Kearney

We show that accelerated gradient descent, averaged gradient descent and the heavy-ball method for non-strongly-convex problems may be reformulated as constant parameter second-order difference equation algorithms, where stability of the…

Machine Learning · Statistics 2015-04-08 Nicolas Flammarion , Francis Bach

In a Hilbert setting we study the convergence properties of a second order in time dynamical system combining viscous and Hessian-driven damping with time scaling in relation with the minimization of a nonsmooth and convex function. The…

Optimization and Control · Mathematics 2022-03-03 Radu Ioan Bot , Mikhail A. Karapetyants

We consider variants of a recently-developed Newton-CG algorithm for nonconvex problems \citep{royer2018newton} in which inexact estimates of the gradient and the Hessian information are used for various steps. Under certain conditions on…

Optimization and Control · Mathematics 2022-04-12 Zhewei Yao , Peng Xu , Fred Roosta , Stephen J. Wright , Michael W. Mahoney

We consider a class of stochastic gradient optimization schemes. Assuming that the objective function is strongly convex, we prove weak error estimates which are uniform in time for the error between the solution of the numerical scheme,…

Numerical Analysis · Mathematics 2026-01-27 Charles-Edouard Bréhier , Marc Dambrine , Nassim En-Nebbazi

In this paper, a second-order accurate method was developed for calculating fluid flows in complex geometries. This method uses cut-Cartesian cell mesh in finite volume framework. Calculus is employed to relate fluxes and gradients along…

Numerical Analysis · Mathematics 2023-04-11 Zhaohui Qin

Nonlinear contraction theory is a comparatively recent dynamic control system design tool based on an exact differential analysis of convergence, in essence converting a nonlinear stability problem into a linear time-varying stability…

Pattern Formation and Solitons · Physics 2007-05-23 Winfried Lohmiller , Jean-Jacques E. Slotine

We present natural axisymmetric variants of schemes for curvature flows introduced earlier by the present authors and analyze them in detail. Although numerical methods for geometric flows have been used frequently in axisymmetric settings,…

Numerical Analysis · Mathematics 2019-11-01 John W. Barrett , Harald Garcke , Robert Nürnberg

Mathematical descriptions of flow phenomena usually come in the form of partial differential equations. The differential operators used in these equations may have properties such as symmetry, skew-symmetry, positive or negative…

Numerical Analysis · Mathematics 2017-10-20 Bas van 't Hof , Mathea J. Vuik

We consider closed, embedded, smooth curves in the plane and study their behaviour under curve flows with a global forcing term. We prove an analogue to Huisken's distance comparison principle for curve shortening flow for initial curves…

Differential Geometry · Mathematics 2022-05-27 Friederike Dittberner

We address in this paper the study of a geometric evolution, corresponding to a curvature which is non-local and singular at the origin. The curvature represents the first variation of the energy recently proposed as a variant of the…

Analysis of PDEs · Mathematics 2012-01-26 Antonin Chambolle , Massimiliano Morini , Marcello Ponsiglione

A fully discrete finite element method, based on a new weak formulation and a new time-stepping scheme, is proposed for the surface diffusion flow of closed curves in the two-dimensional plane. It is proved that the proposed method can…

Numerical Analysis · Mathematics 2021-07-28 Wei Jiang , Buyang Li

Coordinate descent methods have considerable impact in global optimization because global (or, at least, almost global) minimization is affordable for low-dimensional problems. Coordinate descent methods with high-order regularized models…

Optimization and Control · Mathematics 2023-04-28 V. S. Amaral , R. Andreani , E. G. Birgin , D. S. Marcondes , J. M. Martínez

We present a finite-difference scheme which solves the Stokes problem in the presence of curvilinear non-conforming interfaces and provides second-order accuracy on physical field (velocity, vorticity) and especially on pressure. The gist…

Fluid Dynamics · Physics 2011-10-07 Abdelkader Hammouti , Anaël Lemaître

In equality-constrained optimization, a standard regularity assumption is often associated with feasible point methods, namely the gradients of constraints are linearly independent. In practice, the regularity assumption may be violated. To…

Neural and Evolutionary Computing · Computer Science 2020-03-10 Quan Quan , Kai-Yuan Cai
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