The SMF System
SMF (Space Mapping Framework) is a user-friendly, Matlab-based software system that exploits space-mapping technology.  It performs constrained minimax, L2 or custom optimization, engineering model enhancement and statistical analysis.  It is suitable for engineering modeling and design optimization problems.  It contains a number of features that make it especially suitable for microwave engineering.
Features of SMF SM-based Optimization SM-based Modeling Statistical Analysis Interactive SM Direct Coarse Model Optimization Direct Fine Model Optimization Frequency Scaling Space Mapping Optimization with SMF References Contact
Features of SMF
SM-based optimization with flexible surrogate model definition (input/output SM, implicit SM, frequency scaling), and optional trust region methods SM-based local modeling for statistical design and analysis Drivers for the commercial simulators MEFiSTo, Sonnet em, ADS, FEKO Database support for models using commercial simulators SM-based interpolation for evaluating a fine model at off-grid points Direct optimization modules for coarse and surrogate models Interactive SM interface for SM-based modeling, data acquisition, space mapping assessment, statistical analysis, interactive SM optimization, and space mapping tuning Exporting graphs, generating report files Saving project files
	SM-based Optimization
	Visualization of the current status of the optimization algorithm, the fine model response, the design specifications, convergence measures Iteration-by-iteration reviewing of the "history" of the optimization process Optional stopping/resuming of the optimization procedure after any iteration with the same or changed settings Support for matching real and complex responses in parameter extraction
	SM-based Modeling
	Local SM-based modeling for statistical analysis and design User-specified region of interest (model domain) Automatic fine model data acquisition Surrogate model in the form of a self-contained Matlab function Surrogate model testing package for model verification Visualization of fine and surrogate responses, and modeling error
	Statistical Analysis
	Statistical analysis of the fine, coarse or surrogate models Automatic yield estimation with respect to given design specifications and parameter deviation
	Interactive SM
	Flexible space mapping setup Automatic multipoint extraction of space mapping parameters Automatic fine/coarse model data acquisition Space mapping quality assessment (separate procedures for single SM setup, exhaustive SM setup combinations, and implicit SM) Estimation of matching error sensitivity with respect to space mapping parameters SM-based modeling and model testing Interface for generating base/test points Surrogate optimization interface Storing of multiple sets of reference points, fine model responses and space mapping parameters Visualization mode for observing single/multiple responses, error plots, yield estimation and matching error statistics Exporting responses and SM parameters to text and ".mat" files Importing data from SMF databases
	Direct Coarse Model Optimization
	Direct constrained optimization of the coarse model Independent setup of optimization options Possibility of setting the optimization outcome as the starting point for SM optimization
	Direct Fine Model Optimization
	Frequency Scaling
	Nonlinear frequency scaling with polynomials up to order four Manual adjustment of scaling coefficients Automatic single/multi-point extraction of scaling coefficients Frequency scaling assessment interface Embedding of frequency scaling into the coarse model (two modes available)
Space Mapping Optimization with SMF
	A block diagram of the optimization module in SMF is shown here. Optimization is performed in several steps. First, the user enters problem arguments, including starting point, frequency sweep, optimization type and specifications. Next, the user sets up space mapping itself, i.e., the kind of space mapping to be used (e.g., input, output, implicit), specifies the termination condition, parameter extraction options, and optional constraints. The next step is to link the fine and coarse models to SMF by setting up the data that will be used to create the model drivers. Using the user-provided data (e.g., simulator input files and design-parameter identification data), SMF creates drivers that automatically invoke fine and coarse model evaluations as required by the SM algorithm. Parameter extraction, surrogate model optimization, and optional trust-region specific options are set in the next step using auxiliary interfaces. Having done the setup, the user runs the execution interface, which invokes the SM optimization algorithm and the output visualization. The latter includes model responses, specification error plots as well as convergence plots, all updated at each SM iteration. Space mapping optimization with SMF is explained using the example of a microstrip bandpass filter.
References
S. Koziel, J.W. Bandler, and K. Madsen, “Space mapping optimization algorithms for engineering design,” IEEE MTT-S Int. Microwave Symp. Dig. (San Francisco, CA, June 2006), pp. 1601-1604. S. Koziel and J.W. Bandler, “Space-mapping-based modeling utilizing parameter extraction with variable weight coefficients and a data base,” IEEE MTT-S Int. Microwave Symp. Dig. (San Francisco, CA, June 2006), pp. 1763-1766. J. Zhu, J.W. Bandler, N.K. Nikolova, and S. Koziel, “Antenna design through space mapping optimization,” IEEE MTT-S Int. Microwave Symp. Dig. (San Francisco, CA, June 2006), pp. 1605-1608. S. Koziel, J.W. Bandler, and K. Madsen, “Space-mapping based interpolation for engineering optimization,” IEEE Trans. Microwave Theory Tech., vol. 54, no. 6, pp. 2410-2421, June 2006. S. Koziel, J. W. Bandler, and K. Madsen, “A space-mapping framework for engineering optimization—theory and implementation,” IEEE Trans. Microwave Theory Tech., vol. 54, no. 10, pp. 3721-3730, Oct. 2006. S. Koziel and J.W. Bandler, “Space mapping optimization with adaptive surrogate model,” IEEE Trans. Microwave Theory Tech., vol. 55, 2007, accepted. J. Zhu, J.W. Bandler, N.K. Nikolova, and S. Koziel, “Antenna optimization through space mapping,” IEEE Trans. Antennas Propag., vol. 55, 2007, accepted.
Contact john@bandler.com

Last Updated February 19, 2007