Editorial for the special issue on advances in forward and inverse surrogate modeling for high‐frequency design

Abstract

The design of modern-day high-frequency devices and circuits, including microwave/RF, antenna and photonic components, historically has relied on full-wave electromagnetic (EM) simulation tools. Initially used for design verification, EM simulations are nowadays used in the design process itself, for example, for finding optimum values of geometry and/or material parameters of the structures of interest. In a growing number of cases, EM-driven design closure is mandatory because alternative ways of evaluating the circuit performance (such as through equivalent network modeling) are grossly inaccurate and unable to account for cross-coupling effects (eg, in densely arranged layouts of compact circuits or antenna arrays), or various environmental components that affect the circuit performance (eg, connectors or housing for antenna structures). Despite being imperative, simulation-based design poses significant challenges, mostly due to the high computational cost of accurate, high-fidelity analysis. Repetitive simulations entailed by conventional optimization routines and even more by uncertainty quantification procedures (eg, Monte Carlo analysis) or toleranceaware design tasks may generate the costs that are unmanageable or at least impractical. The availability of massive computational resources does not always translate into design speedup due to the need to account for interactions between devices and their surroundings as well as multiphysics (eg, EM-thermal) effects. Not surprisingly, traditional design procedures that directly utilize EM-simulated responses often fail or are impractical. Alternatives to full-wave simulation tools, therefore, are increasingly popular among EM designers. Among the many available options, fast surrogate models that accurately capture the electrical characteristics of the components of interest, recently have received significant attention. Replacing or supplementing EM analysis by the surrogates enables execution of simulation-based design tasks at low computational cost. This is especially the case for data-driven (approximation) models, which are by far the most popular ones due to their versatility and widespread availability. Some broadly used methods include polynomial regression, kriging, radial basis function, neural networks, and polynomial chaos expansion. The practical issue here is nonlinearity of high-frequency component outputs, which along with the curse of dimensionality, hinders utilization of this class of techniques for multiparameter components. Physicsbased surrogates (eg, space mapping or various response correction methods) feature improved generalization capability at the expense of being problem specific: rendering the surrogate normally involves an underlying low-fidelity model, for example, equivalent network or coarse-mesh EM simulation. In addition to that, inverse modeling has been recently fostered as a practical alternative to forward models when solving certain types of design tasks, especially dimension scaling or high-frequency structures. The...