The objective of this paper is to focus on the study of a periodic temporal parabolic equation involving a variable exponent $p(x)$. After proving the existence and uniqueness of the solution, we provide a method for its numerical simulation using emerging deep learning technologies.
In this work, we are interested in the existence, uniqueness, and numerical simulation of weak periodic solutions for some semilinear elliptic equations with data measures and with arbitrary growth of nonlinearities. Since the data are not very regular and the growths are arbitrary, a new approach is needed to analyze these types of equations. Finally, a suitable numerical discretization scheme is presented. Several numerical examples are given which show the robustness of our algorithm.
By applying Mawhin's continuation theorem, theory of Fourier series, Bernoulli numbers theory and some new inequalities, we study the higher-order $p$-Laplacian differential equation with multiple deviating arguments of the form \[(\varphi_{p}(x^{(m)}(t)))^{(m)}= f(x(t))x'(t)+g(t,x(t),x(t-\tau_{1}(t)),\ldots,x(t-\tau_{k}(t)))+e(t).\] Some new results on the existence of periodic solutions for the previous equation are obtained.