Recently, deep learning has been used to establish the nonlinear and nonintuitive mapping between physical structures and electromagnetic responses of meta-atoms for higher computational efficiency. However, to obtain sufficiently accurate predictions, the conventional deep-learning-based method consumes excessive time to collect the data set, thus hindering its wide application in this interdisciplinary field. We introduce a spectral transfer-learning-based metasurface design method to achieve excellent performance on a small data set with only 1000 samples in the target waveband by utilizing open-source data from another spectral range. We demonstrate three transfer strategies and experimentally quantify their performance, among which the “frozen-none” robustly improves the prediction accuracy by ∼26 % compared to direct learning. We propose to use a complex-valued deep neural network during the training process to further improve the spectral predicting precision by ∼30 % compared to its real-valued counterparts. We design several typical teraherz metadevices by employing a hybrid inverse model consolidating this trained target network and a global optimization algorithm. The simulated results successfully validate the capability of our approach. Our work provides a universal methodology for efficient and accurate metasurface design in arbitrary wavebands, which will pave the way toward the automated and mass production of metasurfaces.

We propose a new non-intrusive flow measurement method using the distributed feedback fiber laser (DFB-FL) as a sensor to monitor flow in the pipe. The relationship between the wavelength of the DFB-FL and the liquid flow rate in the pipeline is derived. Under the guidance of this theory, the design and test of the flow sensor is completed. The response curve is relatively flat in the frequency range of 10 Hz to 500 Hz, and the response of the flow sensor has high linearity. The flow from 0.6 m³/h to 25.5 m³/h is accurately measured under the energy analysis method in different frequency intervals. A minimum flow rate of 0.046 m/s is achieved. The experimental results demonstrate the feasibility of the new non-intrusive flow measurement method based on the DFB-FL and accurate measurement of small flow rates.