The paper addresses the problem of computationally efficient electromagnetic (EM)-driven design closure of antenna structures. The foundations of the presented approach are fast kriging interpolation metamodels, utilized for two purposes: (a) producing a good starting point for further parameter tuning, and (b) yielding a reasonable Jacobian matrix estimate to jump-start the optimization procedure. The models are rendered using available designs, for example, obtained from the previous design work with the same antenna structure. The low cost of design closure is ensured by employing Broydenbased trust-region gradient search along with the aforementioned Jacobian initialization. Our methodology is demonstrated using two planar antennas, a dual-band uniplanar dipole and a quasi-Yagi with a parabolic reflector, both optimized within wide ranges of operating conditions (center frequencies, the dielectric permittivity of the antenna substrate). The redesign process requires only a handful of EM analyses of the respective structure. The presented framework can be viewed as a convenient algorithmic tool that capitalizes on the existing information on the structure at hand to enable warm-start parameter tuning. K E Y W O R D S antenna design, design closure, EM simulation, kriging metamodels, parameter tuning 1 | INTRODUCTION Widespread utilization of electromagnetic (EM) simulation tools revolutionized the development procedures of modern antennas. As a matter of fact, EM-driven design is a practical necessity in a growing number of cases where analytical models are no longer adequate. It is especially pertinent to structures featuring nonnegligible mutual coupling effects 1 and feed radiation, 2 compact antennas, 3 wearable antennas at the presence of human body, 4 or situations where environmental components (connectors, housing) affect the antenna operation. 5 Perhaps the most common usage of EM tools is parameter tuning, 6,7 which is a computationally expensive endeavor. Its complexity may be aggravated by factors such as the need for global search, operating in highly-dimensional parameter spaces, or the need for handling multiple performance figures and constraints. 8,9 Convenience, CPU cost and availability of the algorithms (eg, implemented in commercial software packages) typically suggest the utilization of local optimization procedures, but these require reasonably good initial designs, which are not always at hand. Facilitating EM-driven design procedures has been the subject of intense research over the last two decades or so. Among the various techniques developed, some notable examples include adjoint sensitivities and their applications