| _version_ | 1867016883371245569 |
|---|---|
| author | Haoqian Deng Kuiwen Xu Ning Xu Jiangtao Su Xiaokang Qi Shichang Chen Jun Liu Wenjun Li |
| author_facet | Haoqian Deng Kuiwen Xu Ning Xu Jiangtao Su Xiaokang Qi Shichang Chen Jun Liu Wenjun Li Haoqian Deng Kuiwen Xu Ning Xu Jiangtao Su Xiaokang Qi Shichang Chen Jun Liu Wenjun Li |
| collection | Wiley Open Access |
| contents | Learning‐Based Uncertainty Quantification for Inverse Design of Microwave Filter Haoqian Deng Kuiwen Xu Ning Xu Jiangtao Su Xiaokang Qi Shichang Chen Jun Liu Wenjun Li Microwave and Optical Technology Letters ABSTRACT Although the learning‐based surrogate model could achieve fast prediction, the model uncertainty in data and predictions remains a significant challenge. To alleviate the uncertainty problems for the surrogate model, a learning‐based uncertainty analysis method for improving the stability of the inverse model of RF devices is proposed. The inherent uncertainty in the inverse model is analyzed by leveraging ensemble adversarial learning, enabling the prediction of confidence intervals for the inverse surrogate model output without changing the original neural network structure. Subsequently, the differential evolution (DE) algorithm is employed to optimize the geometric parameters of the inverse model output within the predicted interval. To validate feasibility and effectiveness of the method, RF hairpin filter is used for the design example via the neural networks. RF filters with multiple center frequencies and bandwidths are simulated, fabricated and measured. Both simulated and measured results demonstrate a notable enhancement, with a 37.22% predicted accuracy improvement in the degree of fitting between the optimized response and the label of the input network, compared to the circuit response (S‐parameter) pre‐optimization. The proposed method exhibits much better performance in terms of the predicted accuracy and computational time compared to direct optimization without inverse model. The learning‐based method could also be applicable to the other RF devices, affirming the practical applicability and robustness of the approach. 10.1002/mop.70546 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/mop.70546 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_mop_70546 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Learning‐Based Uncertainty Quantification for Inverse Design of Microwave Filter Haoqian Deng Kuiwen Xu Ning Xu Jiangtao Su Xiaokang Qi Shichang Chen Jun Liu Wenjun Li Microwave and Optical Technology Letters Learning‐Based Uncertainty Quantification for Inverse Design of Microwave Filter Haoqian Deng Kuiwen Xu Ning Xu Jiangtao Su Xiaokang Qi Shichang Chen Jun Liu Wenjun Li Microwave and Optical Technology Letters ABSTRACT Although the learning‐based surrogate model could achieve fast prediction, the model uncertainty in data and predictions remains a significant challenge. To alleviate the uncertainty problems for the surrogate model, a learning‐based uncertainty analysis method for improving the stability of the inverse model of RF devices is proposed. The inherent uncertainty in the inverse model is analyzed by leveraging ensemble adversarial learning, enabling the prediction of confidence intervals for the inverse surrogate model output without changing the original neural network structure. Subsequently, the differential evolution (DE) algorithm is employed to optimize the geometric parameters of the inverse model output within the predicted interval. To validate feasibility and effectiveness of the method, RF hairpin filter is used for the design example via the neural networks. RF filters with multiple center frequencies and bandwidths are simulated, fabricated and measured. Both simulated and measured results demonstrate a notable enhancement, with a 37.22% predicted accuracy improvement in the degree of fitting between the optimized response and the label of the input network, compared to the circuit response (S‐parameter) pre‐optimization. The proposed method exhibits much better performance in terms of the predicted accuracy and computational time compared to direct optimization without inverse model. The learning‐based method could also be applicable to the other RF devices, affirming the practical applicability and robustness of the approach. 10.1002/mop.70546 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Learning‐Based Uncertainty Quantification for Inverse Design of Microwave Filter |
| topic | Microwave and Optical Technology Letters |
| url | https://onlinelibrary.wiley.com/doi/10.1002/mop.70546 |