Robustimizer
Surrogate-model based robust optimization software
Include uncertainty in surrogate-based optimization
Communicate with user-defined scripts for automated features
Combine robustness and reliability in optimization
Advanced noise propagation algorithms using analytical methods
Robust Optimization Software Features
Sensitivity analysis for dimension reduction in robust optimization
Design of experiments (DOE) using Latin hypercube sampling (LHS)
Principal component analysis to obtain uncorrelated data
Creation, validation and visualization of surrogate model (Metamodel)
Noise propagation by Monte Carlo (MC) and analytical methods
User-defined samples for DOE and MC for maximum flexibility
Sequential quadratic programming (SQP) or Genetic algorithm (GA)
Definition of reliability constraints in robust optimization
Include higher order moments (Skewness) in robustness and reliability
Sequential improvement of surrogate model using multiple techniques
Automatic update of surrogate model with user-defined scripts
Robustimizer tutorials
Robustimizer is a software tool to perform optimization under the influence of uncertainties. Classical deterministic optimization neglects the uncertainties which are present in model input parameters. However, If some of those inputs are hard to control or out of control, the uncertainties will be present in the output. In this case the objective is to find a setting of controllable parameters at which the noise parameters have the least influence on the output. Usually a mathematical model or a surrogate model of the process is required to perform this type of optimization. Therefore, an in-depth understanding of the system is required. To learn more about robust optimization using Robustimizer follow these links:
Introduction to Robustimizer
Example 1: Mathematical function
Example 2: Basketball free throw
Learn more about advanced features of Robustimizer
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