Moments, Time-Inversion and Source Identification for the Heat Equation

TL;DR

This paper introduces a novel moment-based method for the ill-posed inverse problem of identifying initial sources in the heat equation, significantly improving stability and providing explicit error estimates.

Contribution

The authors develop a new approach using moment analysis and convex optimization to stabilize the inverse heat problem, reducing exponential error growth to polynomial growth.

Findings

Reduces exponential error growth to polynomial with respect to terminal time.

Provides explicit error estimates based on moment order and measurement errors.

Demonstrates improved stability through numerical experiments.

Abstract

We address the initial source identification problem for the heat equation, a notably ill-posed inverse problem characterized by exponential instability. Departing from classical Tikhonov regularization, we propose a novel approach based on moment analysis of the heat flow, transforming the problem into a more stable inverse moment formulation. By evolving the measured terminal time moments backward through their governing ODE system, we recover the moments of the initial distribution. We then reconstruct the source by solving a convex optimization problem that minimizes the total variation of a measure subject to these moment constraints. This formulation naturally promotes sparsity, yielding atomic solutions that are sums of Dirac measures. Compared to existing methods, our moment-based approach reduces exponential error growth to polynomial growth with respect to the terminal time. We provide explicit error estimates on the recovered initial distributions in terms of moment order, terminal time, and measurement errors. In addition, we develop efficient numerical discretization schemes and demonstrate significant stability improvements of our approach through comprehensive numerical experiments.