ﻻ يوجد ملخص باللغة العربية
In this paper, we characterize meromorphic solutions $f(z_1,z_2),g(z_1,z_2)$ to the generalized Fermat Diophantine functional equations $h(z_1,z_2)f^m+k(z_1,z_2)g^n=1$ in $mathbf{C}^2$ for integers $m,ngeq2$ and nonzero meromorphic functions $h(z_1,z_2),k(z_1,z_2)$ in $mathbf{C}^2$. Meromorphic solutions to associated partial differential equations are also studied.
The concept of moment differentiation is extended to the class of moment summable functions, giving rise to moment differential properties. The main result leans on accurate upper estimates for the integral representation of the moment derivatives of
We investigate the existence of non-trivial holomorphic and meromorphic solutions of Fermat functional equations over an open Riemann surface $S$. When $S$ is hyperbolic, we prove that any $k$-term Fermat functional equation always exists non-trivial
In this paper, we prove that the only primitive solutions of the equation $a^2+3b^6=c^n$ for $ngeq 3$ are $(a,b,c,n)=(pm 47,pm 2,pm 7,4)$. Our proof is based on the modularity of Galois representations of $mathbb Q$-curves and the work of Ellenberg f
Let $mathcal{X}$ be a real separable Hilbert space. Let $Q$ be a linear, self-adjoint, positive, trace class operator on $mathcal{X}$, let $F:mathcal{X}rightarrowmathcal{X}$ be a (smooth enough) function and let ${W(t)}_{tgeq 0}$ be a $mathcal{X}$-va
In this paper we establish a connection between non-convex optimization methods for training deep neural networks and nonlinear partial differential equations (PDEs). Relaxation techniques arising in statistical physics which have already been used s