Learning‐Based Uncertainty Quantification for Inverse Design of Microwave Filter

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 fo