Applications

This section of jupyter book covers different outer loop applications using the models described in modeling section. The section starts with describing Bayesian optimization (BO), which combines Gaussian process model with appropriate acquisition function for efficiently solving computationally expensive problems. Next, BO is extended to handle constrained optimization problems. Then, this section describes uncertainty analysis process using surrogate models for estimating randomness in quantities of interest due to uncertain inputs. Lastly, optimization under uncertainty is covered that involves solving optimization problems where objective function and/or constraints can be uncertain.

This section covers following topics:

1. Bayesian optimization

2. Constrained Bayesian optimization

3. Uncertainty analysis

4. Optimization under uncertainty