Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy
arXiv:1901.10819
Abstract
The research of metamaterials has achieved enormous success in the manipulation of light in an artificially prescribed manner using delicately designed sub-wavelength structures, so-called meta-atoms. Even though modern numerical methods allow to accurately calculate the optical response of complex structures, the inverse design of metamaterials is still a challenging task due to the non-intuitive and non-unique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial design, we propose to represent metamaterials and model the inverse design problem in a probabilistically generative manner. By employing an encoder-decoder configuration, our deep generative model compresses the meta-atom design and optical response into a latent space, where similar designs and similar optical responses are automatically clustered together. Therefore, by sampling in the latent space, the stochastic latent variables function as codes, from which the candidate designs are generated upon given requirements in a decoding process. With the effective latent representation of metamaterials, we can elegantly model the complex structure-performance relationship in an interpretable way, and solve the one-to-many mapping issue that is intractable in a deterministic model. Moreover, to alleviate the burden of numerical calculation in data collection, we develop a semi-supervised learning strategy that allows our model to utilize unlabeled data in addition to labeled data during training, simultaneously optimizing the generative inverse design and deterministic forward prediction in an end-to-end manner. On a data-driven basis, the proposed model can serve as a comprehensive and efficient tool that accelerates the design, characterization and even new discovery in the research domain of metamaterials and photonics in general.
28 pages, 5 figures
References in corpus (10)
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Searching for Exotic Particles in High-Energy Physics with Deep Learning
- Training deep neural networks for the inverse design of nanophotonic structures
- Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer
- A Generative Model for Inverse Design of Metamaterials
- Electromagnetically Induced Transparency and Absorption in Metamaterials: The Radiating Two-Oscillator Model and Its Experimental Confirmation
- Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks
- Deep Neural Network Inverse Design of Integrated Nanophotonic Devices
- Predicting resonant properties of plasmonic structures by deep learning