User guide# Make GEMSEO easy to use Interface a software Define an MDO problem Define settings models Cheat sheets Extend GEMSEO features Main concepts Design space Discipline Scenario Data persistence Optimization and DOE The GEMSEO concepts Optimization and DOE framework Optimization algorithms DOE algorithms Design of experiments API Algorithms Advanced use Multidisciplinary design optimization Coupling visualization MDO formulations Multi Disciplinary Analyses How to deal with post-processing Machine learning Introduction API Unsupervised learning for unlabeled data Supervised learning for input-output data Measure the quality of a machine learning algorithm Calibrate or select a machine learning algorithm Transform data to improve the ML algorithm quality Surrogate models Create a surrogate discipline Uncertainty quantification Introduction Probability distribution Parameter space Statistics Sensitivity analysis Ordinary differential equations Introduction ODEProblem ODEDiscipline Scalable models Scalable models The scalable problem Comprehensive list of tools and their settings Disciplines DOE (design of experiments) Linear solvers Machine learning MDA (multidisciplinary analysis) MDO formulations ODE (ordinary differential equation) solvers Optimizers Post-processors Surrogate disciplines Uncertainty Benchmark problems Sellar's problem Aerostructure problem Sobieski's SSBJ test case The Propane combustion problem
