Jinyi Liu

This letter proposes a novel resonance degree discrimination algorithm for the systematic compact embedded neuro-transfer function (SC-TF) parametric modeling method. As the variation range of geometrical parameters expands, the electromagnetic (EM) responses of microwave components exhibit stronger fluctuations, which lead to the emergence of abnormal pole–residue pairs and inconsistent distributions of high- and low-resonance sub-TFs under different geometrical parameters. To address these issues, a three-step strategy is introduced, which incorporates preliminary pole–residue classification, sub-TF scaling, and adaptive pole–residue tracking. The proposed algorithm effectively improves the modeling accuracy and applicability range of the SC-TF method. The effectiveness of the proposed algorithm is verified through two microwave filter examples.

The vector fitting (VF) technique is popularly used in neural network-transfer function (neuro-TF) modeling methods for electromagnetic (EM) behaviors of microwave filters. However, VF often produces nonsmooth and discontinuous transfer function (TF) coefficients under large geometrical variations, which degrade training stability and model accuracy. To address this issue, this article proposes a novel autoencoder-based neural network-TF (AENN-TF) parametric modeling method for EM behavior of microwave filters. In the proposed method, an advanced autoencoder (AE)-based feature parameter extraction technique is incorporated to develop a smooth relationship of TF parameters with respect to geometrical variables. This smooth relationship improves the neural network training accuracy, consequently improving the overall AENN-TF modeling accuracy for EM behavior. Novel derivative formulations are derived for the proposed model structure in order to perform fast gradient-based overall model training. Furthermore, we develop a novel four-step training framework with detailed modeling processes for the proposed AENN-TF modeling method. Compared to existing neuro-TF methods with the VF technique, the proposed AENN-TF method achieves better accuracy and robustness. The superior performance of the proposed method over existing neuro-TF methods is further demonstrated through two microwave application examples.

Recent research has demonstrated the exponential potential of hybrid quantum–classical algorithms (HQAs) in solving electromagnetic (EM) problems. However, the optimization objective of HQAs typically focuses on the magnitude of electric fields in EM problems, neglecting phase information. Consequently, the resulting quantum solution is inherently normalized to unity and subject to a global phase offset. This limitation prevents the calculation of the correct magnitude and phase of S-parameters. To address this challenge, we propose a block-encoding-based HQA (BEHQA) integrating a specialized quantum method for S-parameter extraction with magnitude and phase. The proposed method employs the specialized quantum method to reconstruct both the magnitude scaling and the phase offset, ensuring correct S-parameter extraction. Besides, HQAs fundamentally operate on unitary matrices, necessitating the explicit decomposition of nonunitary finite element method (FEM) matrices into many unitary operators. To mitigate the overhead associated with matrix decomposition, we introduce the block-encoding to embed the FEM matrix directly as a sub-block within a unitary operator. This strategy eliminates the need for decomposing FEM matrices to unitary operators, thereby reducing circuit complexity. For efficient optimization, we also derive a novel cost function specifically designed for the proposed BEHQA. The algorithm employs a strongly entangling ansatz (SEA) and gradient-based optimization to minimize the loss function. The effectiveness of BEHQA is demonstrated through its application to two microwave components, showcasing its feasibility for quantum-driven EM simulations.

This article proposes a novel multifeature-assisted electromagnetic (EM) surrogate model optimization method based on the complex frequency domain (CFD) EM parameter extraction technique. By introducing a CFD-based fast frequency sweep, the method enables efficient extraction of internal features from EM parameters, thereby facilitating the construction of high-accuracy parametric surrogate models and supporting advanced design of microwave filters. In scenarios where the initial design response is far from the target or the EM response lacks clarity, a feature-assisted optimization strategy is adopted to guide the design process. By exploiting multiple feature parameters and introducing a novel objective function, the proposed method effectively relocates the passband into the desired design specifications. The novel feature parameter extraction technique based on CFD can solve the problem of poor optimization performance in traditional surrogate models caused by the multiple-solution issue in vector-fitting (VF) parameter extraction. Compared to conventional surrogate model optimization methods, the proposed method achieves faster convergence for practical high-level design applications.

Quantum computing in the electromagnetic (EM) field represents a transformative frontier with significant potential for advancement. Recent studies have demonstrated the potential of quantum computing for solving waveguide mode problems, solving finite element method (FEM) equations for EM problems, and scaling the FEM solution. Among these applications, the Harrow–Hassidim–Lloyd (HHL) algorithm is an algorithm with large quantum advantages and has attracted attention. However, existing quantum methods remain constrained by the problem that the quantum-based FEM solution only obtains electric field magnitude and lacks phase information. The lack of phase information results in the inability to solve an important characteristic of EM problems: scattering parameters (S-parameters). To address the above issue, this work proposes a novel quantum method of calculating multiport S-parameter magnitudes based on FEM and HHL algorithm for microwave components. The proposed method introduces novel mathematical formulations optimized for quantum architectures. To streamline the quantum computation process and avoid the need for multiple quantum circuit simulations for each S-parameter, we design an integrated quantum circuit capable of computing magnitudes of all multiport S-parameters within a single execution. Furthermore, a specific matrix-scaling method is also developed to simplify the proposed circuits. The specific matrix-scaling method is used to improve the condition of the FEM matrix and reduce the complexity of the designed quantum circuits. The proposed method is validated using two microwave structures, demonstrating how our proposed quantum method computes S-parameter magnitudes in practical EM applications.

Electromagnetic (EM) optimization of microwave components often relies on repeated full-wave simulations, which can be time-consuming even for routine design tasks. Existing acceleration techniques such as mesh space mapping (MSM) typically require manual coarse-mesh tuning and coarse–fine fitting, limiting automation and reproducibility. To address these issues, this article presents a multilevel model order reduction (MOR) framework that derives a reduced-order coarse model (ROCM) directly from the fine model, thereby avoiding mesh coarsening and empirical fitting. The first level enables rapid broadband evaluation via frequency-domain reduction; the second reuses a shared projection basis across nearby geometries with adaptive updates when surrogate error grows. Geometry changes are accommodated through mesh deformation, and sensitivities are computed efficiently by restricting adjoint-based updates to perturbed elements. The proposed method is validated on multiple waveguide filter designs and compared against direct fine-mesh optimization, single-level MOR, and MSM. Across the reported cases, the proposed method achieves the shortest total runtime while maintaining fine-model accuracy and stable convergence, eliminating the need for coarse-mesh construction and surrogate fitting. These results demonstrate a robust, automated workflow that combines high fidelity with substantial computational savings over practical design ranges.