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Gradient-flow (GF) viewpoints unify and illuminate optimization algorithms, yet most GF analyses focus on unconstrained settings. We develop a geometry-respecting framework for constrained problems by (i) reparameterizing feasible sets with…

Optimization and Control · Mathematics 2025-08-29 Valentin Leplat

This paper presents a new and efficient nodal scheme for cell-centered compressible flows in Lagrangian formulation. A single pressure and the velocity at cell vertex are computed by the nodal solver in a least-squares sense, and both…

Computational Physics · Physics 2020-01-14 Chuanjin Wang , Hong Luo

This paper introduces a new method for proving global stability of fluid flows through the construction of Lyapunov functionals. For finite dimensional approximations of fluid systems, we show how one can exploit recently developed…

Optimization and Control · Mathematics 2015-05-20 Paul Goulart , Sergei Chernyshenko

In this work, we focus on separable convex optimization problems with box constraints and a set of triangular linear constraints. The solution is given in closed-form as a function of some Lagrange multipliers that can be computed through…

Information Theory · Computer Science 2015-06-22 Antonio A. D'Amico , Luca Sanguinetti , Daniel P. Palomar

We present an energy-stable scheme for numerically approximating the governing equations for incompressible two-phase flows with different densities and dynamic viscosities for the two fluids. The proposed scheme employs a scalar-valued…

Computational Physics · Physics 2019-06-26 Z. Yang , S. Dong

The unique solvability and error analysis of the original Lagrange multiplier approach proposed in [8] for gradient flows is studied in this paper. We identify a necessary and sufficient condition that must be satisfied for the nonlinear…

Numerical Analysis · Mathematics 2024-05-07 Qing Cheng , Jie Shen , Cheng Wang

We propose an improved version of the PAMPA algorithm where the solution is sought as globally continuous. The scheme is locally conservative, and there is no mass matrix to invert. This method had been developed in a series of papers, see…

Numerical Analysis · Mathematics 2026-05-06 Rémi Abgrall , Yongle Liu

We propose a new family of high order staggered semi-implicit discontinuous Galerkin (DG) methods for the simulation of natural convection problems. Assuming small temperature fluctuations, the Boussinesq approximation is valid and the flow…

Computational Physics · Physics 2019-12-05 Saray Busto , Maurizio Tavelli , Walter Boscheri , Michael Dumbser

In this paper, a semi-Lagrangian gas-kinetic scheme is developed for smooth flows based on the Bhatnagar-Gross-Krook (BGK) equation. As a finite-volume scheme, the evolution of the average flow variables in a control volume is under the…

Computational Physics · Physics 2014-12-10 Peng Wang , Zhaoli Guo

In this paper, we conduct an in-depth investigation of the structural intricacies inherent to the Invariant Energy Quadratization (IEQ) method as applied to gradient flows, and we dissect the mechanisms that enable this method to uphold…

Numerical Analysis · Mathematics 2023-06-13 Yukun Yue

In this work, we study shape optimization problems in the Stokes flows. By phase-field approaches, the resulted total objective function consists of the dissipation energy of the fluids and the Ginzburg--Landau energy functional as a…

Numerical Analysis · Mathematics 2022-07-13 Futuan Li , Jiang Yang

The continuous nonlinear resource allocation problem (CONRAP) has broad applications in economics, engineering, production and inventory management, and often serves as a subproblem in complex programming. Without relying on monotonicity…

Optimization and Control · Mathematics 2025-01-10 Kaixiang Hu , Caixia Kou , Jianhua Yuan

Invariant discretization schemes are derived for the one- and two-dimensional shallow-water equations with periodic boundary conditions. While originally designed for constructing invariant finite difference schemes, we extend the usage of…

Mathematical Physics · Physics 2013-01-04 Alexander Bihlo , Roman O. Popovych

In this paper, for the first time we propose two linear, decoupled, energy-stable numerical schemes for multi-component two-phase compressible flow with a realistic equation of state (e.g. Peng-Robinson equation of state). The methods are…

Numerical Analysis · Mathematics 2017-12-12 Jisheng Kou , Shuyu Sun , Xiuhua Wang

This paper introduces a novel approach to compute the numerical fluxes at the cell boundaries in the finite volume approach. Explicit gradients used in deriving the reconstruction polynomials are replaced by high-order gradients computed by…

Numerical Analysis · Mathematics 2021-06-04 Amareshwara Sainadh Chamarthi , Steven H. Frankel , Abhishek Chintagunta

We propose two closely--related Lagrangian numerical methods for the simulation of physical processes involving advection, reaction and diffusion. The methods are intended to be used in settings where the flow is nearly incompressible and…

Computational Physics · Physics 2018-03-14 Francesco Paparella , Marina Popolizio

This work focuses on the numerical approximation of the Shallow Water Equations (SWE) using a Lagrange-Projection type approach. We propose to extend to this context recent implicit-explicit schemes developed in the framework of…

Numerical Analysis · Mathematics 2016-07-05 Christophe Chalons , Pierre Kestener , Samuel Kokh , Maxime Stauffert

In this work, we propose a new semi-Lagrangian (SL) finite difference scheme for nonlinear advection-diffusion problems. To ensure conservation, which is fundamental for achieving physically consistent solutions, the governing equations are…

Numerical Analysis · Mathematics 2025-11-05 Silvia Preda , Walter Boscheri , Matteo Semplice , Maurizio Tavelli

In this paper we propose and analyze a second order accurate (in time) numerical scheme for the square phase field crystal (SPFC) equation, a gradient flow modeling crystal dynamics at the atomic scale in space but on diffusive scales in…

Numerical Analysis · Mathematics 2021-01-01 Min Wang , Qiumei Huang , Cheng Wang

Deep generative models hold great promise for representing complex physical systems, but their deployment is currently limited by the lack of guarantees on the physical plausibility of the generated outputs. Ensuring that known physical…

Machine Learning · Computer Science 2026-03-13 Matthieu Blanke , Yongquan Qu , Sara Shamekh , Pierre Gentine
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