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In this paper we study high order expansions of chart maps for local finite dimensional unstable manifolds of hyperbolic equilibrium solutions of scalar parabolic partial differential equations. Our approach is based on studying an infinitesimal invariance equation for the chart map that recovers the dynamics on the manifold in terms of a simple conjugacy. We develop formal series solutions for the invariance equation and efficient numerical methods for computing the series coefficients to any desired finite order. We show, under mild non-resonance conditions, that the formal series expansion converges in a small enough neighborhood of the equilibrium. An a-posteriori computer assisted argument proves convergence in larger neighborhoods. We implement the method for a spatially inhomogeneous Fishers equation and numerically compute and validate high order expansions of some local unstable manifolds for morse index one and two. We also provide a computer assisted existence proof of a saddle-to-sink heteroclinic connecting orbit.
Let ${u(t,,x)}_{tge 0, xin mathbb{R}^d}$ denote the solution of a $d$-dimensional nonlinear stochastic heat equation that is driven by a Gaussian noise, white in time with a homogeneous spatial covariance that is a finite Borel measure $f$ and satisf
Our aim in this paper is to establish stable manifolds near hyperbolic equilibria of fractional differential equations in arbitrary finite dimensional spaces.
We consider a steady state $v_{0}$ of the Euler equation in a fixed bounded domain in $mathbf{R}^{n}$. Suppose the linearized Euler equation has an exponential dichotomy of unstable and center-stable subspaces. By rewriting the Euler equation as an O
We investigate methods for learning partial differential equation (PDE) models from spatiotemporal data under biologically realistic levels and forms of noise. Recent progress in learning PDEs from data have used sparse regression to select candidate
Motivated by recent research on Physics-Informed Neural Networks (PINNs), we make the first attempt to introduce the PINNs for numerical simulation of the elliptic Partial Differential Equations (PDEs) on 3D manifolds. PINNs are one of the deep learn