We describe an LSTM-based autoencoder for inversely designing an achromatic metalens comprised of cylindrical unit cells. The training data for our model has phase and transmission values corresponding to the heights and radii of each meta-unit. We use multiple data sequences (phase and transmission) to train the model and a multi-output model framework. The autoencoder is trained for 2500 iterations using the Adam optimizer with a learning rate of 0.001 and is subsequently used for inversely predicting the meta-unit dimensions at each radial position of the lens. Our model is validated via simulations as well as experiments.
Physics informed neural networks (PINNs) solve supervised learning tasks by incorporating partial differential equations describing the governing physics. We use a PINN based on Maxwell’s equations in the frequency domain to predict the electrical permittivity parameters, and hence the electric fields, of circular split-ring resonator-based metamaterials thereby bypassing full-wave solutions based on finite-element methods. We demonstrate the use of a PINN for the inverse prediction of the electrical permittivity of a circular split ring resonator metamaterial given the spatial e-field distributions at the resonant frequency. Our results validate the PINN framework for the inverse retrieval of permittivities from field distributions.
We report a framework comprising of a combination of sequence models (e.g. RNN, LSTM, Bi LSTM etc) and deep neural networks (DNNs) to tackle the forward problem of predicting the optical response for a given geometry of a broadband, terahertz metamaterial absorber based on Au split-ring resonators. We obtained a training and validation losses of 0.0062 and 0.0042 respectively. The test dataset for this model yielded a loss of 0.0026. Using our model, we were able to predict the spectral response of similar metamaterial absorber geometries in less than 0.5 seconds.
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