Online optimisation for dynamic electrical impedance tomography
This work addresses real-time monitoring challenges in fluid dynamics, but it appears incremental as it builds on existing online optimisation and EIT methods.
The authors tackled the problem of real-time monitoring of moving bodies in a fluid using Electrical Impedance Tomography (EIT) by proposing a primal dual online optimisation method for nonlinear time-discrete inverse problems, analyzing it through regret theory and demonstrating its performance in this application.
Online optimisation studies the convergence of optimisation methods as the data embedded in the problem changes. Based on this idea, we propose a primal dual online method for nonlinear time-discrete inverse problems. We analyse the method through regret theory and demonstrate its performance in real-time monitoring of moving bodies in a fluid with Electrical Impedance Tomography (EIT). To do so, we also prove the second-order differentiability of the Complete Electrode Model (CEM) solution operator on $L^\infty$.