On Adaptive Frequency Sampling for Data-driven Model Order Reduction Applied to Antenna Responses

TL;DR

This paper introduces two adaptive frequency sampling methods that leverage the Loewner framework to efficiently reduce the number of full-wave simulations needed for accurate antenna response modeling in the frequency domain.

Contribution

The paper presents two novel adaptive algorithms exploiting the Loewner framework's block matrix function for improved frequency point selection in model order reduction.

Findings

Both methods effectively estimate frequency domain errors.

Algorithms achieve smaller errors with fewer frequency points.

Numerical experiments validate the efficiency of the proposed approaches.

Abstract

Frequency domain sweeps of array antennas are well-known to be time-intensive, and different surrogate models have been used to improve the performance. Data-driven model order reduction algorithms, such as the Loewner framework and vector fitting, can be integrated with these adaptive error estimates, in an iterative algorithm, to reduce the number of full-wave simulations required to accurately capture the requested frequency behavior of multiport array antennas. In this work, we propose two novel adaptive methods exploiting a block matrix function which is a key part of the Loewner framework generating system approach. The first algorithm leverages an inherent matrix parameter freedom in the block matrix function to identify frequency points with large errors, whereas the second utilizes the condition number of the block matrix function. Both methods effectively provide frequency domain error estimates, which are essential for improved performance. Numerical experiments on multiport array antenna S-parameters demonstrate the effectiveness of our proposed algorithms within the Loewner framework, where the proposed algorithms reach the smallest errors for the smallest number of frequency points chosen.