Surrogate model

The process comprises three major steps which may be interleaved iteratively: The accuracy of the surrogate depends on the number and location of samples (expensive experiments or simulations) in the design space.Popular surrogate modeling approaches are: polynomial response surfaces; kriging; more generalized Bayesian approaches;[1] gradient-enhanced kriging (GEK); radial basis function; support vector machines; space mapping;[2] artificial neural networks and Bayesian networks.[2][7] Recently proposed comparison-based surrogate models (e.g., ranking support vector machines) for evolutionary algorithms, such as CMA-ES, allow preservation of some invariance properties of surrogate-assisted optimizers:[8] An important distinction can be made between two different applications of surrogate models: design optimization and design space approximation (also known as emulation).Depending on the type of surrogate used and the complexity of the problem, the process may converge on a local or global optimum, or perhaps none at all.[9] In design space approximation, one is not interested in finding the optimal parameter vector, but rather in the global behavior of the system.
mathematical modelairfoildesign optimizationdesign space explorationsensitivity analysisblack-boxcurve fittingoptimal experimental designactive learningbias-variance tradeoffdesign of experimentsresponse surfaceskrigingBayesiangradient-enhanced krigingradial basis functionsupport vector machinesspace mappingartificial neural networksBayesian networksFourierrandom forestsspace-mappingevolutionary algorithmsCMA-ESmonotonic transformationsorthogonal transformationsgenetic algorithmglobal optimumPythonderivativesgradientspline interpolationtree-based modelsGaussian processLinear approximationResponse surface methodologyRadial basis functionsSurrogate endpointSurrogate dataFitness approximationComputer experimentConceptual modelBayesian regressionBayesian model selectionJ.W. BandlerBibcodeShyy, W.