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Section Navigation

  • 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
  • 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
  • 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
  • Make GEMSEO easy to use
  • Define settings models

Define settings models#

Learn how to define the settings for formulations, algorithms, MDAs and post-processings.

  • Formulation Settings
  • Algorithm Settings
  • MDA Settings
  • Post-processor Settings

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