Hierarchical active learning for defect localization in 3D systems

Abstract

Aim: Advanced sensing and imaging is capable to retrieve rich information of complex systems, which can be integrated with underlying physics to develop a personalized simulation framework to discern system internal property for defect localization. Simulation-based defect localization involves modeling the heterogeneous 3D physical systems and calibrating spatial-varying model parameters. In this paper, we aim to develop an effective simulation-based active learning framework for defect localization in complex 3D systems. Methods: We develop a Hierarchical Gaussian Process (GP)-based Active Learning (HGPAL) framework to estimate the spatial-varying model parameters for reliable defect localization in complex 3D systems. We first develop a GP regression model on the 2D embedding of a 3D geometry to capture the geometric information. Second, we propose a Hierarchical GP-based active learning approach composed of master GP and sub-GP modeling to account for the system heterogeneity and further estimate the spatial-varying model parameter for defect localization. Results: We evaluate the performance of the proposed HGPAL framework in a 3D body-heart system to identify and localize infarct in the heart. Numerical experiments demonstrate the effectiveness of our HGPAL framework for 3D defect localization. Conclusion: This study successfully developed and tested an HGPAL framework for defect localization in complex 3D systems. The results suggest that this approach can efficiently and accurately localize defects in 3D systems, demonstrating its potential for practical applications in precision engineering system design and healthcare treatment planning.