Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexity

Abstract

Here, we present a new approach based on manifold learning for knowledge discovery and inverse design with minimal complexity in photonic nanostructures. Our approach builds on studying sub-manifolds of responses of a class of nanostructures with different design complexities in the latent space to obtain valuable insight about the physics of device operation to guide a more intelligent design. In contrast to the current methods for inverse design of photonic nanostructures, which are limited to pre-selected and usually over-complex structures, we show that our method allows evolution from an initial design towards the simplest structure while solving the inverse problem.

arXiv comment10 pages, 6 figures, 2 tables

References in corpus (20)

• Deep Learning with Coherent Nanophotonic Circuits
• Outlook for inverse design in nanophotonics
• Training deep neural networks for the inverse design of nanophotonic structures
• Deep neural networks for the evaluation and design of photonic devices
• All-optical Reservoir Computing
• Deep learning in nano-photonics: inverse design and beyond
• Discovering physical concepts with neural networks
• Freeform Diffractive Metagrating Design Based on Generative Adversarial Networks
• Metasurface Optics for Full-color Computational Imaging
• Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures
• Mapping the global design space of nanophotonic components using machine learning pattern recognition
• Inverse design and implementation of a wavelength demultiplexing grating coupler
• Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices
• Elucidating the Behavior of Nanophotonic Structures Through Explainable Machine Learning Algorithms
• Knowledge Discovery In Nanophotonics Using Geometric Deep Learning
• Towards high-power, high-coherence, integrated photonic mmWave platform with microcavity solitons
• Deep learning enabled design of complex transmission matrices for universal optical components
• Mapping the Design Space of Photonic Topological States via Deep Learning
• Fast Design of Plasmonic Metasurfaces Enabled by Deep Learning
• Benchmarking deep inverse models over time, and the neural-adjoint method

Cited by in corpus (9)

• Data-Driven Design for Metamaterials and Multiscale Systems: A Review
• Intelligent metaphotonics empowered by machine learning
• Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design
• A newcomer's guide to deep learning for inverse design in nano-photonics
• Deep neural networks for the prediction of the optical properties and the free-form inverse design of metamaterials
• Deep learning for nano-photonic materials -- The solution to everything!?
• Design of ultracompact broadband focusing spectrometers based on deep diffractive neural networks
• Fast approximate solvers for metamaterials design in electromagnetism
• A general framework for knowledge integration in machine learning for electromagnetic scattering using quasinormal modes