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EM Optimization using Coarse and Fine Mesh Space Mapping

EM Optimization using Coarse and Fine Mesh Space Mapping. Chao Zhang. Introduction. Space mapping is a recognized engineering optimization methodology in the microwave area. The coarse models : empirical functions , equivalent circuits computationally efficient low accuracy

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EM Optimization using Coarse and Fine Mesh Space Mapping

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  1. EM Optimization using Coarse and Fine Mesh Space Mapping Chao Zhang

  2. Introduction Space mapping is a recognized engineering optimization methodology in the microwave area. The coarse models : empirical functions ,equivalent circuits computationally efficient low accuracy The fine models: provided by an electromagnetic (EM) simulator accurate computationally intensive Space mapping combines the computational efficiency of coarse models with the accuracy of fine models.

  3. Algorithm Models The fine model: finemesh EM simulation high accuracy achieved by the mesh convergence process The coarse model: coarsemesh EM local meshing without the mesh convergence process.

  4. Algorithm Notions the neural network weight parameters in the neural network the optimal parameters of the neural network

  5. Algorithm Functions The original EM optimization: (1) Build an input space mapping neural network: (2) Establish a surrogate model: (3) Unit mapping: train (2) to learn . Finding the optimal solution: (4) Error function: (5) Termination condition: (6)

  6. Algorithm Functions Optimize our surrogate model to match the fine model data. The error function for this optimization is defined as (7) The solution of the optimization: (8) Next we perform the neural network training, (9) Perform the surrogate design optimization. Then perform fine model evaluation and repeat (3)-(9). When termination condition is satisfied, the algorithm stops.

  7. Algorithm Flowchart

  8. Examples A. Bandpass HTS Microstrip Filter (a) (b) The bandpass microstrip filter example: the coarse mesh and fine mesh EM simulations of this structure are used as the coarse and fine models, respectively. Space mapping optimization on bandpass filter. The comparison of the coarse and fine mesh models at initial point. Responses of the fine model from each iteration of the space mapping optimization.

  9. Examples A. Bandpass HTS Microstrip Filter Comparison of Direct Fine Model Optimization and Proposed Space Mapping Optimization

  10. Examples B. Two-Section Lowpass Elliptic Microstrip Filter (a) (b) The lowpass elliptic microstrip filter example: the coarse mesh and fine mesh EM simulations of this structure are used as the coarse and fine models, respectively. Space mapping optimization on lowpass filter. The comparison of the coarse and fine mesh models at initial point. Responses of the fine model from each iteration of the space mapping optimization.

  11. Examples B. Two-Section Lowpass Elliptic Microstrip Filter Comparison of Direct Fine Model Optimization and Proposed Space Mapping Optimization

  12. Conclusion 1. The method uses coarse and fine mesh EM evaluations. 2. The coarse model: coarse mesh EM simulation. 3. Useful when equivalent coarse model is not available. 4. Much more efficient than direct fine mesh EM optimization.

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