Skip to main content
Ctrl+K
gemseo homepage gemseo homepage
  • User guide
  • Examples
  • API documentation
  • Cheat sheets
  • About us
    • Bibliography
    • Contributing
    • Credits
    • FAQ
    • General index
    • Glossary
    • Licenses
    • Overview
    • Plugins
    • Roadmap
    • Software
    • Upgrading
  • GitLab
  • Discourse
  • User guide
  • Examples
  • API documentation
  • Cheat sheets
  • About us
  • Bibliography
  • Contributing
  • Credits
  • FAQ
  • General index
  • Glossary
  • Licenses
  • Overview
  • Plugins
  • Roadmap
  • Software
  • Upgrading
  • GitLab
  • Discourse

Section Navigation

  • Make GEMSEO easy to use
    • Global configuration
    • 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
  • 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
  • User guide
  • Optimization and DOE

Optimization and DOE#

  • The GEMSEO concepts
    • Design space
    • Optimization problem
    • Driver
  • Optimization and DOE framework
    • Setting up an OptimizationProblem
    • Solving the problem by optimization
    • Solving the problem by DOE
    • Results analysis
    • DOE algorithms
  • Optimization algorithms
    • Augmented_Lagrangian_order_0
    • Augmented_Lagrangian_order_1
    • COBYQA
    • DIFFERENTIAL_EVOLUTION
    • DUAL_ANNEALING
    • DUAL_SIMPLEX
    • HEXALY
    • INTERIOR_POINT
    • L-BFGS-B
    • MMA
    • MNBI
    • MultiStart
    • NELDER-MEAD
    • NLOPT_BFGS
    • NLOPT_BOBYQA
    • NLOPT_COBYLA
    • NLOPT_MMA
    • NLOPT_NEWUOA
    • NLOPT_SLSQP
    • PDFO_BOBYQA
    • PDFO_COBYLA
    • PDFO_NEWUOA
    • PYMOO_GA
    • PYMOO_NSGA2
    • PYMOO_NSGA3
    • PYMOO_RNSGA3
    • PYMOO_UNSGA3
    • PYOPTSPARSE_SLSQP
    • PYOPTSPARSE_SNOPT
    • SHGO
    • SLSQP
    • SMT_EGO
    • Scipy_MILP
    • TNC
  • DOE algorithms
    • CustomDOE
    • DiagonalDOE
    • Halton
    • LHS
    • MC
    • MorrisDOE
    • OATDOE
    • OT_AXIAL
    • OT_COMPOSITE
    • OT_FACTORIAL
    • OT_FAURE
    • OT_FULLFACT
    • OT_HALTON
    • OT_HASELGROVE
    • OT_LHS
    • OT_LHSC
    • OT_MONTE_CARLO
    • OT_OPT_LHS
    • OT_RANDOM
    • OT_REVERSE_HALTON
    • OT_SOBOL
    • OT_SOBOL_INDICES
    • PYDOE_BBDESIGN
    • PYDOE_CCDESIGN
    • PYDOE_FF2N
    • PYDOE_FULLFACT
    • PYDOE_LHS
    • PYDOE_PBDESIGN
    • PoissonDisk
    • Sobol

previous

ZvsXY

next

The GEMSEO concepts

This Page

  • Show Source

© Copyright 2025, IRT Saint Exupéry.