Abstract: Yield estimation of antenna systems is important to check their robustness with respect to the uncertain sources. Since the Monte Carlo sampling-based real physics simulation model evaluations are computationally intensive, this work proposes the polynomial chaos-Kriging (PC-Kriging) metamodeling technique for fast yield estimation. PC-Kriging integrates the polynomial chaos expansion (PCE) as the trend function of Kriging metamodel since the PCE is good at cap-turing the function tendency and Kriging is good at matching the observations at training points. The PC-Kriging is demonstrated with an analytical case and a multi-band patch antenna case and compared with direct PCE and Kriging meta-models. In the analytical case, PC-Kriging reduces the computational cost by around 42% compared with PCE and over 94% compared with Kriging. In the antenna case, PC-Kriging reduces the computational cost by over 60% compared with Kriging and over 90% compared with PCE. In both cases, the savings are obtained without compromising the accuracy.

Abstract: Quasi-Newton methods are popular gradient-based optimization methods that can achieve rapid convergence using only first-order derivatives. However, the choice of the initial Hessian matrix upon which quasi-Newton updates are applied is an important factor that can significantly affect the performance of the method. This fact is especially true for limited-memory variants, which are widely used for large-scale problems where only a small number of updates are applied in order to minimize the memory footprint. In this paper, we introduce both a scalar and a sparse diagonal Hessian initialization framework, and we investigate its effect on the restricted Broyden-class of quasi-Newton methods. Our implementation in PETSc/TAO allows us to switch between different Broyden class methods and Hessian initializations at runtime, enabling us to quickly perform parameter studies and identify the best choices. The results indicate that a sparse Hessian initialization based on the diagonalization of the BFGS formula significantly improves the base BFGS methods and that other parameter combinations in the Broyden class may offer competitive performance.

Abstract: Electromagnetic (EM) analysis is the primary tool utilized in the design of high-frequency structures. In the vast majority of cases, simpler models (e.g., equivalent networks or analytical ones) are either not available or lack accuracy: they can only be used to yield initial designs that need to be further tuned. Consequently, EM-driven adjustment of geometry and/or material parameters of microwave and antenna components is a necessary design stage. This, however, is a computationally expensive process, not only because of the considerable computational cost of high-fidelity EM analysis but also due to a typically large number of parameters that need to be adjusted. In particular, conventional numerical optimization routines (both local and global) may be prohibitively expensive. In this paper, a reduced-cost trust-region-based gradient search algorithm is proposed for optimization of high-frequency components. Our methodology is based on a smart management of the system Jacobian enhancement which combines omission of (finite-differentiation-based) sensitivity updates for variables that exhibit small (relative) relocation in the directions of the corresponding coordinate system axes and selective utilization of a rank-one Broyden updating formula. Parameter selection for Broyden-based updating depends on the alignment between the direction of the latest design relocation and respective search space basis vectors. The proposed technique is demonstrated using an ultra-wideband antenna and a miniaturized coupler. In both cases, a significant reduction of the number of EM simulations involved in the optimization process is achieved as compared to the benchmark algorithm. At the same time, degradation of the design quality is minor.

Abstract: Reducing the measurement effort that is made for identification of parameters is an important task in some fields of technology. This work focuses on calibration of functions running on the electronic control unit (ECU), where measurements are the main expense factor. An algorithm for information content analysis of recorded measurement data is introduced that places the calibration engineer in the position to shorten future test runs. The analysis is based upon parameter sensitivities and utilizes the Fisher-information matrix to determine the value of certain measurement portions with respect to parameter identification. By means of a simple DC motor model the algorithm's working principle is illustrated. The first use on a real ECU function achieves a measurement time reduction of 67% while a second use case opens up new features for the calibration of connected cars.

Abstract: Large scale cell based blood flow simulations are expensive, both in time and resource requirements. HemoCell can perform such simulations on high performance computing resources by dividing the simulation domain into multiple blocks. This division has a performance impact caused by the necessary communication between the blocks. In this paper we implement an efficient algorithm for computing the mechanical model for HemoCell together with an improved communication structure. The result is an up to $4$ times performance increase for blood flow simulations performed with HemoCell.