# Regularized solutions for some backward nonlinear parabolic equations with statistical data

## TL;DR

This paper develops new regularization methods for solving ill-posed backward nonlinear parabolic equations with noisy data, providing convergence analysis for various equations with constant and time-dependent coefficients.

## Contribution

The paper introduces novel regularization techniques for backward nonlinear parabolic equations with statistical data, applicable to multiple equations including heat, Fisher-Kolmogorov, and Fitzhugh-Nagumo.

## Key findings

- Established convergence rates for regularized solutions
- Applicable to a wide class of nonlinear parabolic equations
- Methods handle random noise in data

## Abstract

In this paper, we study the backward problem of determining initial condition for some class of nonlinear parabolic equations in multidimensional domain where data are given under random noise. This problem is ill-posed, i.e., the solution does not depend continuously on the data. To regularize the instable solution, we develop some new methods to construct some new regularized solution. We also investigate the convergence rate between the regularized solution and the solution of our equations. In particular, we establish results for several equations with constant coefficients and time dependent coefficients. The equations with constant coefficients include heat equation, extended Fisher-Kolmogorov equation, Swift-Hohenberg equation and many others. The equations with time dependent coefficients include Fisher type Logistic equations, Huxley equation, Fitzhugh-Nagumo equation. The methods developed in this paper can also be applied to get approximate solutions to several other equations including 1-D Kuramoto-Sivashinsky equation, 1-D modified Swift-Hohenberg equation, strongly damped wave equation and 1-D Burger's equation with randomly perturbed operator.