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

ABSTRACT: 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

SUBMITTER: Zhu E 

PROVIDER: S-EPMC11885849 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature