Journal Article

PUBDB-2025-00253

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge

Munteanu, V. (Corresponding author)Extern* ; Starostin, V. ; Greco, A. ; Pithan, L.DESY* ; Gerlach, A. ; Hinderhofer, A. ; Kowarik, S. ; Schreiber, F. (Corresponding author)Extern*

2024
Munksgaard Copenhagen

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Please use a persistent id in citations: doi:10.1107/S1600576724002115  doi:10.3204/PUBDB-2025-00253

Abstract: Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.

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Contributing Institute(s):

1. FS-Experiment Control (FS-EC)

Research Program(s):

1. 623 - Data Management and Analysis (POF4-623) (POF4-623)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2024

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DEAL Wiley ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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