A low-rank tensor method to reconstruct sparse initial states for PDEs with Isogeometric Analysis

Abstract

When working with PDEs the reconstruction of a previous state often proves difficult. Good prior knowledge and fast computational methods are crucial to build a working reconstruction. We want to identify the heat sources on a three dimensional domain from later measurements under the assumption of small, distinct sources, such as hot chippings from a milling tool. This leads us to the need for a Prior reflecting this a priori information. Sparsity-inducing hyperpriors have proven useful for similar problems with sparse signal or image reconstruction. We combine the method of using a hierarchical Bayesian model with gamma hyperpriors to promote sparsity with low-rank computations for PDE systems in tensor train format.