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Conformal metasurfaces with flexible structures are able to fit complicated platforms which have obvious advantages in moving platforms scattering manipulations. However, conformal metasurfaces far-field simulations is high time consumption and hard to optimize, making the its agile designing difficult. Here, an efficient and intelligent scattering field calculation method is proposed based on transfer learning for conformal metasurfaces. Firstly, according to the uniformity in physical mechanism between antenna theory and full wave simulation, the initial mapping model between phase distribution and far-field of metasurface is constructed and pre-trained based on a large amount of theoretical data in source domain. Secondly, by pre-training, parameter freezing and model fine-tuning, the far-field prediction model for full wave simulation is transferred and achieved successfully, based on small amount full wave simulation data in target domain. Finally, the transfer learning model for far-field prediction is transferred once again for conformal metasurfaces with different structures. Results indicate that, the proposed method only consumes 0.1% time of full wave simulation for conformal metasurface far-field calculation. In cases of fewer samples, the model with transfer learning can improve the average accuracy by 19.8%, training data account for only 42.9% for the that of model without transfer learning, which reduce training data collection time by 50.1%. Moreover, our far-field calculation method demonstrates good transfer performance between conformal metasurfaces with different structures.
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Keywords:
- conformal metasurface /
- transfer learning /
- scattering field
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