Passive Macromodeling: a Review of Cross-Disciplinary Applications

Invited lectur

Passive Macromodeling: a Review of Cross-Disciplinary Applications

Invited lectur

Robust transfer function identification via an enhanced magnitude vector fitting algorithm

The study introduces an enhanced version of the magnitude vector fitting (magVF) algorithm, a robust procedure for the identification of a transfer function from magnitude frequency domain data. The approach is based on the rational approximation of the magnitude square function with enforcement of symmetric poles and zeros, followed by the elimination of poles and zeros located in the right half-plane. The obtained transfer function is stable and of minimum-phase shift type. Robustness and accuracy of the basic magVF algorithm are enhanced by enforcing that the magnitude square rational function is non-negative definite and by introducing a new method to remove purely imaginary conjugate poles from the approximation. Practical application of the proposed approach is demonstrated for measured transformer responses and transmission line propagation functions

An Introduction to Superconducting Qubits and Circuit Quantum Electrodynamics

A subset of the concepts of circuit quantum electrodynamics are reviewed as a reference to the Axion Dark Matter Experiment (ADMX) community as part of the proceedings of the 2nd Workshop on Microwave Cavities and Detectors for Axion Research. The classical Lagrangians and Hamiltonians for an LC circuit are discussed along with black box circuit quantization methods for a weakly anharmonic qubit coupled to a resonator or cavity

Interpolatory methods for H∞\mathcal{H}_\infty model reduction of multi-input/multi-output systems

We develop here a computationally effective approach for producing high-quality $\mathcal{H}_\infty$-approximations to large scale linear dynamical systems having multiple inputs and multiple outputs (MIMO). We extend an approach for $\mathcal{H}_\infty$ model reduction introduced by Flagg, Beattie, and Gugercin for the single-input/single-output (SISO) setting, which combined ideas originating in interpolatory $\mathcal{H}_2$-optimal model reduction with complex Chebyshev approximation. Retaining this framework, our approach to the MIMO problem has its principal computational cost dominated by (sparse) linear solves, and so it can remain an effective strategy in many large-scale settings. We are able to avoid computationally demanding $\mathcal{H}_\infty$ norm calculations that are normally required to monitor progress within each optimization cycle through the use of "data-driven" rational approximations that are built upon previously computed function samples. Numerical examples are included that illustrate our approach. We produce high fidelity reduced models having consistently better $\mathcal{H}_\infty$ performance than models produced via balanced truncation; these models often are as good as (and occasionally better than) models produced using optimal Hankel norm approximation as well. In all cases considered, the method described here produces reduced models at far lower cost than is possible with either balanced truncation or optimal Hankel norm approximation

On tuning passive black-box macromodels of LTI systems via adaptive weighting

This paper discusses various approaches for tuning the accuracy of rational macromodels obtained via black-box identification or approximation of sampled frequency responses of some unknown Linear and Time-Invariant system. Main emphasis is on embedding into the model extraction process some information on the nominal terminations that will be connected to the model during normal operation, so that the corresponding accuracy is optimized. This goal is achieved through an optimization based on a suitably defined cost function, which embeds frequency-dependent weights that are adaptively refined during the model construction. A similar procedure is applied in a postprocessing step for enforcing model passivity. The advantages of proposed algorithm are illustrated on a few application examples related to power distribution networks in electronic system

Data-driven model order reduction using orthonormal vector fitting

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The association of selected multiple sclerosis symptoms with disability and quality of life:a large Danish self-report survey

Abstract Background People with multiple sclerosis (MS) experience a wide range of unpredictable and variable symptoms. The symptomatology of MS has previously been reported in large sample registry studies; however, some symptoms may be underreported in registries based on clinician-reported outcomes and how the symptoms are associated with quality of life (QoL) are often not addressed. The aim of this study was to comprehensively evaluate the frequency of selected MS related symptoms and their associations with disability and QoL in a large self-report study. Methods We conducted a cross-sectional questionnaire survey among all patients at the Danish Multiple Sclerosis Center, Copenhagen University Hospital, Denmark. The questionnaire included information on clinical and sociodemographic characteristics, descriptors of QoL and disability, as well as prevalence and severity of the following MS symptoms: impaired ambulation, spasticity, chronic pain, fatigue, bowel and bladder dysfunction, and sleep disturbances. Results Questionnaires were returned by 2244/3606 (62%). Participants without MS diagnosis or incomplete questionnaires were excluded, n = 235. A total of 2009 questionnaires were included for analysis (mean age 49.4 years; mean disease duration 11.7 years; and 69% were women). The most frequently reported symptoms were bowel and bladder dysfunction (74%), fatigue (66%), sleep disturbances (59%), spasticity (51%) and impaired ambulation (38%). With exception of fatigue and sleep disturbances, all other symptoms increased in severity with higher disability level. Invisible symptoms (also referred to as hidden symptoms) such as fatigue, pain and sleep disturbances had the strongest associations with the overall QoL. Conclusion We found invisible symptoms highly prevalent, even at mild disability levels. Fatigue, pain and sleep disturbances had the strongest associations with the overall QoL and were more frequently reported in our study compared with previous registry-based studies. These symptoms may be underreported in registries based on clinician reported outcomes, which emphasizes the importance of including standardized patient reported outcomes in nationwide registries to better understand the impact of the symptom burden in MS