Space mapping

The space mapping methodology for modeling and design optimization of engineering systems was first discovered by John Bandler in 1993.The space mapping methodology employs a "quasi-global" formulation that intelligently links companion "coarse" (ideal or low-fidelity) and "fine" (practical or high-fidelity) models of different complexities.After the validation process, if the design specifications are not satisfied, relevant data is transferred to the optimization space ("feedback"), where the mapping-augmented coarse model or surrogate is updated (enhanced, realigned with the fine model) through an iterative optimization process termed "parameter extraction".Following John Bandler's concept in 1993,[1][2] algorithms have utilized Broyden updates (aggressive space mapping),[3] trust regions,[4] and artificial neural networks.The space mapping technique has been applied in a variety of disciplines including microwave and electromagnetic design, civil and mechanical applications, aerospace engineering, and biomedical research.
engineering systemsJohn Bandleroptimizationcoarse spacesurrogate modelfeedbackartificial neural networkslarge-signalstatistical modelingnonlinearmicrowaveradio frequencymachine learningintuitioninverse problemselectromagneticaerospace engineeringcrashworthinessmicrowave circuitselectric machinespartial differential equationsStructural optimizationmicrowave filters and multiplexersPower electronicsSignal integrityCivil engineeringKeysightMomentumAnsys HFSSsimulationmultigrid methodAdaptive controlCognitive modelComputational electromagneticsComputer-aided designEngineering optimizationFinite element methodKrigingLinear approximationMental modelMental rotationMirror neuronModel-dependent realismMultiphysicsPerformance tuningResponse surface methodologySemiconductor device modelingSpatial cognitionSpatial memorySupport vector machineTheory of mindWayback MachineC. Caloz