The application of robust model-based design concepts for complex chemical processes is limited due to the repeated cpu-intensive uncertainty quantification step for any new tested process design configuration.
A variety of OR techniques are available to address problems in human resource planning, hiring, and training in knowledge-intensive operations under uncertainty [ 28- 30].
Bootstrapping is a simple, widely used, but computationally intensive method for estimating uncertainty in statistics of interest [20], [21].
Solution of large order fluid structure interaction (FSI) systems with parametric uncertainties demand intensive computational resources.
The inclusion of such uncertainties demands intensive studies in space and time of engineering structures under environmental and mechanical stressors.
However, these intensive modeling efforts include uncertainties in their parameterization, potentially limiting their extensibility to larger regions [51], or assume uniformity of soils globally [52].
This is due to the fact that it is a capital intensive investment associated with great uncertainty.
However, limited understanding of processes, data limitations and parameter uncertainty, and computationally intensive numerical solutions have reduced the interest to perform detailed field-scale modeling, especially with non-linear Monod expressions.
For these reasons, robust linear and nonlinear beamforming (precoding) designs to combat against channel uncertainty have received intensive research interests for different multi-user multiple-input multiple-output (MIMO) (single-/multi-cell downlink or relay) systems [8 19].
A better but much more computationally intensive method to measure parameter uncertainty, which we call the inverse-sensitivity approach, directly evaluates the sensitivity of inverse estimates of model parameters with respect to the calibration targets.
The bootstrap is a computationally intensive method for assessing the uncertainty in a statistical measure [15].
