2,825 results for equation · 0.134s

arxiv.org/abs/1202.6265v1

Discretely holomorphic parafermions and integrable boundary conditions

In two-dimensional statistical models possessing a discretely holomorphic parafermion, we introduce a modified discrete Cauchy-Riemann equation on the boundary of the domain, and we show that the solution of this equation yields integrable boundary B...

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arxiv.org/abs/2503.00989v2

A four-field mixed formulation for incompressible finite elasticity

In this work, we generalize the mass-conserving mixed stress (MCS) finite element method for Stokes equations [Gopalakrishnan J., Lederer P., and Schöberl J., A mass conserving mixed stress formulation for the Stokes equations, IMA Journal of Numeri...

arxiv.org/abs/1301.0251v1

Lucas Type Theorem Modulo Prime Powers

In this note we prove that {equation*} {np^s\choose mp^s+r}\equiv (-1)^{r-1}r^{-1}(m+1){n\choose m+1}p^s \pmod{p^{s+1}} {equation*} where $p$ is any prime, $n$, $m$, $s$ and $r$ are nonnegative integers such that $n\ge m$, $s\ge 1$, $1\le r\le p^...

arxiv.org/abs/2310.15695v1

Lie minimal Weingarten surfaces

We consider Lie minimal surfaces, the critical points of the simplest Lie sphere invariant energy, in Riemannian space forms. These surfaces can be characterized via their Euler-Lagrange equations, which take the form of differential equations of the...

arxiv.org/abs/2304.10493v1

Algebraic calming for the 2D Kuramoto-Sivashinsky equations

We propose an approximate model for the 2D Kuramoto-Sivashinsky equations (KSE) of flame fronts and crystal growth. We prove that this new ``calmed'' version of the KSE is globally well-posed, and moreover, its solutions converge to solutions of the...

arxiv.org/abs/1607.04540v1

Kazdan-Warner equation on graph

Let $G=(V,E)$ be a finite graph and $Δ$ be the usual graph Laplacian. Using the calculus of variations and a method of upper and lower solutions, we give various conditions such that the Kazdan-Warner equation $Δu=c-he^u$ has a solution on $V$, whe...

arxiv.org/abs/2103.15341v3

Stiff Neural Ordinary Differential Equations

Neural Ordinary Differential Equations (ODE) are a promising approach to learn dynamic models from time-series data in science and engineering applications. This work aims at learning Neural ODE for stiff systems, which are usually raised from chemic...