Frequency transfer and inverse design for metasurface under multi-physics coupling by Euler latent dynamic and data-analytical regularizations

Frequency transfer is a key challenge in machine learning as it allows researchers to go beyond in-range analyses of spectrum properties towards out-of-the-range predictions. Traditionally, to predict properties at a specific frequency, targeted spectrum is included in training data for a deep neural network (DNN). However, due to limitations of measurement or computation source, training data at some frequencies are hardly accessible, especially for multi-physics problems. In this work, we propose a multi-physics deep learning framework (MDLF) consisting of a multi-fidelity DeepONet, a Euler latent dynamic network, and a data-analytical inversion network. Without the knowledge about multi-physics response, MDLF is successfully generalized to unseen frequency bands for both parametric and free-form metasurface by dynamically utilizing a Euler latent space and single-physics information. Moreover, an inversion method is introduced to incorporate hybrid a priori in inverse design of metasurface. Under EM-thermal coupling, we verify the proposed MDLF numerically and experimentally. Frequency transfer, which allows researchers to go beyond in-range analyses of spectrum properties towards out-of-the range predictions, is a key challenge in machine learning. Here, the authors propose a multi-physics deep learning framework without the knowledge of the multi-physics response to inverse design metasurface.