Work

PENTAGON: Physics-enhanced neural network for volumetric flame chemiluminescence tomography

Year: 2024

Type: article

Abstract: This study proposes a physics-enhanced neural network, PENTAGON, as an inference framework for volumetric tomography applications. By leveraging the synergistic combination of data-prior and forward-imaging model, we can accurately predict 3D optical fields, even when the number of projection views decreases to three. PENTAGON is proven to overcome the generalization limitation of data-driven deep learning methods due to data distribution shift, and eliminate distortions introduced by conventional iteration algorithms with limited projections. We evaluated PENTAGON using numerical and experimental results of a flame chemiluminescence tomography example. Results showed that PENTAGON can potentially be generalized for inverse tomography reconstruction problems in many fields.

Source: Optics Express

DOI: 10.1364/oe.536550

Authors: Ying Jin, Sunyong Zhu, Shouyu Wang, Fei Wang, Quanying Wu, Guohai Situ

Institutions: Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics, Wuxi University, Suzhou University of Science and Technology

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: National Natural Science Foundation of China, Program of Shanghai Academic Research Leader