Presentation
2 August 2021 Opening the black box for data efficiency and inverse design in photonics
Raphaël Pestourie, Steven G. Johnson
Author Affiliations +
Abstract
Supervised neural networks are rising as an algorithm of choice for surrogate models in photonics, because they are versatile, fast to evaluate, easily differentiable, and perform well in high-dimensional problems. However, the drawback of this black box approach is that it requires a lot of data. Unfortunately in the context of photonics, data is generated through expensive full solves of Maxwell’s equations. This talk will present ways to open the black box for better data efficiency and performance of deep surrogate models. The first part of this talk will present how active learning can reduce the need for data by at least an order of magnitude by adapting the data generation to the model learning. The second part will present how information about the physics can be incorporated into the neural network for more efficiency.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Raphaël Pestourie and Steven G. Johnson "Opening the black box for data efficiency and inverse design in photonics", Proc. SPIE 11795, Metamaterials, Metadevices, and Metasystems 2021, 1179503 (2 August 2021); https://doi.org/10.1117/12.2592805
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KEYWORDS
Photonics

Data modeling

Neural networks

Evolutionary algorithms

Maxwell's equations

Performance modeling

Physics

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