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GEMSEO 5.3.2
Please
cite us
if you use the software.
User guide
Make GEMSEO easy to use
Main concepts
Optimization and DOE
Design of experiments
Multidisciplinary design optimization
Machine learning
Surrogate models
Uncertainty quantification
Scalable models
Algorithms
Benchmark problems
User guide
¶
Make GEMSEO easy to use
High-level functions to use GEMSEO
Interface a software
Define an MDO problem
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
Scalable models
Scalable models
The scalable problem
Algorithms
DOE algorithms
Linear solvers
Algorithms of machine learning
MDA algorithms
MDO formulations
Ordinary differential equations solvers
Optimization algorithms
Post-processing algorithms
Surrogate disciplines
Uncertainty algorithms
Benchmark problems
Sellar’s problem
Aerostructure problem
Sobieski’s SSBJ test case
The Propane combustion problem