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| 001 | 1048804 | ||
| 005 | 20251217202227.0 | ||
| 024 | 7 | _ | |a 10.48550/ARXIV.2511.07633 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-04917 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-04917 |
| 100 | 1 | _ | |a Bangun, Arya |0 P:(DE-Juel1)184644 |b 0 |u fzj |
| 245 | _ | _ | |a FlowTIE: Flow-based Transport of Intensity Equation for Phase Gradient Estimation from 4D-STEM Data |
| 260 | _ | _ | |c 2025 |b arXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1765992836_12985 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to DataCite |
| 650 | _ | 7 | |a Machine Learning (cs.LG) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Töllner, Maximilian |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Zhao, Xuan |0 P:(DE-Juel1)200005 |b 2 |u fzj |
| 700 | 1 | _ | |a Kübel, Christian |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Scharr, Hanno |0 P:(DE-Juel1)129394 |b 4 |u fzj |
| 773 | _ | _ | |a 10.48550/ARXIV.2511.07633 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1048804/files/2511.07633v1.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2025 |
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| 980 | _ | _ | |a preprint |
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| 980 | _ | _ | |a UNRESTRICTED |
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