2019
DOI: 10.1002/mmce.21714
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Numerically efficient algorithm for compact microwave device optimization with flexible sensitivity updating scheme

Abstract: An efficient trust-region algorithm with flexible sensitivity updating management scheme for electromagnetic (EM)-driven design optimization of compact microwave components is proposed. During the optimization process, updating of selected columns of the circuit response Jacobian is performed using a rank-one Broyden formula (BF) replacing finite differentiation (FD). The FD update is omitted for directions sufficiently well aligned with the recent design relocation. As the algorithm converges, the alignment t… Show more

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Cited by 27 publications
(13 citation statements)
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“…The methods for accelerating EM-driven design procedures have been researched for over two decades [6], [7]. Available approaches include incorporation of adjoint sensitivities [8], [9], or sparse Jacobian updates [10], [11] into gradient-based algorithms, utilization of custom EM solvers [12], as well as mesh deformation techniques [13]. Other methods rely on variable-fidelity or variable-resolution techniques [14], [15], often combined with physics-based surrogate modeling procedures (e.g., space mapping [16], response correction [17], Bayesian model fusion [18], cokriging [19]).…”
Section: Introductionmentioning
confidence: 99%
“…The methods for accelerating EM-driven design procedures have been researched for over two decades [6], [7]. Available approaches include incorporation of adjoint sensitivities [8], [9], or sparse Jacobian updates [10], [11] into gradient-based algorithms, utilization of custom EM solvers [12], as well as mesh deformation techniques [13]. Other methods rely on variable-fidelity or variable-resolution techniques [14], [15], often combined with physics-based surrogate modeling procedures (e.g., space mapping [16], response correction [17], Bayesian model fusion [18], cokriging [19]).…”
Section: Introductionmentioning
confidence: 99%
“…On a practical side, multiobjective design is often handled by reformulating the problem into a single‐objective task . The reason is strictly opportunistic: the abundance of single‐objective optimization methods and their reliable implementations .…”
Section: Introductionmentioning
confidence: 99%
“…On a practical side, multiobjective design is often handled by reformulating the problem into a singleobjective task. 35 The reason is strictly opportunistic: the abundance of single-objective optimization methods and their reliable implementations. [36][37][38] The mentioned reformulation can be realized either through objective aggregation (weighted sum methods 39 and goal attainment method 40 ) or by casting all but a primary goal into constraints.…”
Section: Introductionmentioning
confidence: 99%
“…A conceptually straightforward approach (yet complex implementation-wise) is to incorporate adjoint sensitivities into gradient-based algorithms [15], [16] although availability of this technology is limited throughout commercial simulation packages. Another strictly algorithmic method is to suppress finitedifferentiation sensitivity updates, e.g., by monitoring Jacobian variability [17] or design relocation [18]. In recent years, utilization of fast surrogate models has been gaining considerable popularity.…”
Section: Introductionmentioning
confidence: 99%