Turning: Autonomous process set-up through Bayesian optimization and Gaussian process models

Bayesian treed Gaussian process models

This paper explores nonparametric and semiparametric nonstationary modeling methodologies that couple stationary Gaussian processes and (limiting) linear models with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. Mixing between full Gaussian processes and simple linear models can yield a more parsimonious spatial model while significantly red...

Fast Bayesian Inference for Gaussian Process Models

In many engineering and science disciplines, deterministic computer models or codes are used to simulate complex physical processes. The computer code mathematically describes the relationship between several input variables and one or more output variables. Often the computer models in question can be computationally demanding. Thus, direct evaluation of the code for optimization or validation...

Optimization of Turning Process through Taguchi and Simulated Annealing Algorithm

High Dimensional Bayesian Optimization with Elastic Gaussian Process

Bayesian optimization depends on solving a global optimization of a acquisition function. However, the acquisition function can be extremely sharp at high dimension having only a few peaks marooned in a large terrain of almost flat surface. Global optimization algorithms such as DIRECT are infeasible at higher dimensions and gradient-dependent methods cannot move if initialized in the flat terr...

Global Optimization Employing Gaussian Process-Based Bayesian Surrogates

The simulation of complex physics models may lead to enormous computer running times. Since the simulations are expensive it is necessary to exploit the computational budget in the best possible manner. If for a few input parameter settings an output data set has been acquired, one could be interested in taking these data as a basis for finding an extremum and possibly an input parameter set fo...