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Model society magazine pdf12/7/2023 ![]() ![]() ![]() ![]() Results indicate that Bayesian optimization using a Gaussian process surrogate model converges rapidly on an approximated optimal solution. Various use cases and parameterizations are discussed, including the effects of the choice of acquisition function and covariance function of the Gaussian process. Optimization is performed with sequential model evaluations, with an acquisition function guiding the next point in parameter space to be evaluated. The surrogate model represents the posterior on the objective function and is updated with each model evaluation. April McKay lives in relentless pursuit of epic imagery. In this book April shares a series of epic and almost unbelievable adventures in pursuit of artistic beauty. Replica fields are obtained using a normal mode propagation model whose geoacoustic parameters are selected from the parameter search space. Across 160 beautiful pages, youll join April McKay on a great adventure through modeling, photography, and life. The objective function is defined as a Bartlett processor whose output measures the match between a received and replica pressure field on a vertical line array. In this study, we present a method that samples geoacoustic parameter space with a Bayesian approach that uses a Gaussian process as a surrogate model of the objective function. ![]()
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