%0 Electronic Article
%A Bangun, Arya
%A Töllner, Maximilian
%A Zhao, Xuan
%A Kübel, Christian
%A Scharr, Hanno
%T FlowTIE: Flow-based Transport of Intensity Equation for Phase Gradient Estimation from 4D-STEM Data
%I arXiv
%M FZJ-2025-04917
%D 2025
%X We introduce FlowTIE, a neural-network-based framework for phase reconstruction from 4D-Scanning Transmission Electron Microscopy (STEM) data, which integrates the Transport of Intensity Equation (TIE) with a flow-based representation of the phase gradient. This formulation allows the model to bridge data-driven learning with physics-based priors, improving robustness under dynamical scattering conditions for thick specimen. The validation on simulated datasets of crystalline materials, benchmarking to classical TIE and gradient-based optimization methods are presented. The results demonstrate that FlowTIE improves phase reconstruction accuracy, fast, and can be integrated with a thick specimen model, namely multislice method.
%K Machine Learning (cs.LG) (Other)
%K FOS: Computer and information sciences (Other)
%F PUB:(DE-HGF)25
%9 Preprint
%R 10.48550/ARXIV.2511.07633
%U https://juser.fz-juelich.de/record/1048804