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24 May 2022 Improved solutions to optical inverse problems by neural networks and prior assumptions
Taavi Repän, Yannick Augenstein, Carsten Rockstuhl
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Proceedings Volume 12130, Metamaterials XIII; 1213008 (2022) https://doi.org/10.1117/12.2621548
Event: SPIE Photonics Europe, 2022, Strasbourg, France
Abstract
We use convolutional neural networks (CNN) to predict scattering geometry from the fields outside of the scatterer. While this problem is nonunique, we show that by training on specific datasets, the CNN learns the underlying structure of the scatterers. I.e., if there is prior knowledge of the expected structure or form of the scatterers, this can be used to obtain a much more accurate solution to the inverse scattering problem. We show that our method faithfully recovers the original geometry for highly specific classes of structures, while the more conventional method falls victim to the nonuniqueness and fails to recover plausible-looking geometries.
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Taavi Repän, Yannick Augenstein, and Carsten Rockstuhl "Improved solutions to optical inverse problems by neural networks and prior assumptions", Proc. SPIE 12130, Metamaterials XIII, 1213008 (24 May 2022); https://doi.org/10.1117/12.2621548
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KEYWORDS
Inverse problems
Inverse optics
Neural networks
Convolutional neural networks
Scattering
Scientific research
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