Enhanced shape recovery in advection--diffusion problems via a novel ADMM-based CCBM optimization

TL;DR

This paper introduces a new shape optimization framework for inverse advection--diffusion problems using CCBM and ADMM, improving obstacle reconstruction accuracy and robustness against noise.

Contribution

It develops a novel ADMM-based CCBM optimization method with explicit shape derivatives, enhancing shape recovery in complex advection--diffusion inverse problems.

Findings

Accurate shape reconstruction demonstrated in numerical experiments.

Robustness against measurement noise improved.

Efficient computation via adjoint and partial gradient methods.

Abstract

This work proposes a novel shape optimization framework for geometric inverse problems governed by the advection--diffusion equation, based on the coupled complex boundary method (CCBM). Building on recent developments [Afr22, Rab23, Rab25, RAN25, RN24], we aim to recover the shape of an unknown inclusion via shape optimization driven by a cost functional constructed from the imaginary part of the complex-valued state variable over the entire domain. We rigorously derive the associated shape derivative in variational form and provide explicit expressions for the gradient and second-order information. Optimization is carried out using a Sobolev gradient method within a finite element framework. To address difficulties in reconstructing obstacles with concave boundaries, particularly under measurement noise and the combined effects of advection and diffusion, we introduce a state-of-the-art numerical scheme inspired by the Alternating Direction Method of Multipliers (ADMM). In addition to implementing this non-conventional approach, we demonstrate how the adjoint method can be efficiently applied and utilize partial gradients todevelop a more efficient CCBM-ADMM scheme. The accuracy and robustness of the proposed computational approach are validated through various numerical experiments.