Main Concepts
Discipline
Define an input-output discipline to interface a model.
Interfaces: analytic expressions, executable, surrogate model, much more.
Design space
Define a set of parameters, typically design parameters.
Types: deterministic parameter space, uncertain (or mixed) parameter space
Scenario
Define an evaluation process over a design space for a set of disciplines and a given objective.
Types: DOE scenario, MDO scenario
Features
Study Prototyping
An intuitive tool to discover MDO without writing any code, and define the right MDO problem and process. From an Excel workbook, specify your disciplines, design space, objective and constraints, select an MDO formulation and plot both coupling structure (N2 chart) and MDO process (XDSM), even before wrapping any software.
DOE & Trade-off
Define, solve and post-process a trade-off problem from a DOE (design of experiments) algorithm.
Design of experiments: axial, bilevel full-factorial, Box-Behnken, central-composite, composite, custom, diagonal, Faure, full factorial, Halton, Haselgrove, LHS, Monte-Carlo, Plackett-Burman, reverse Halton, Sobol
MDO formulations
Define the way as the disciplinary coupling is formulated and managed by the optimization or DOE algorithm.
Formulations: bilevel, IDF, MDF, standard optimization
MDA
Find the coupled state of a multidisciplinary system using a Multi-Disciplinary Analysis.
Algorithms: Gauss-Seidel, Jacobi, MDA chain, Newton-Raphson, Quasi-Newton, Gauss-Seidel/Newton
Visualization
Generate graphical post-processings of optimization histories.
Visualizations:: basic history, constraint history, correlations, gradient sensitivity, k-means, objective and constraint history, optimization history view, parallel coordinates, quadratic approximation, radar chart, robustness, scatter matrix, self organizing map, variable influence.
Surrogate models
Replace a discipline by a surrogate one relying on a machine learning regression model.
Surrogate models: Gaussian process regression (kriging), linear model, radial basis regression, polynomial chaos expansion and surrogate quality measures.
based on scikit-learn and OpenTURNS
Scalable models
Use scalable data-driven models to compare MDO formulations and algorithms for different problem dimensions.
Features: scalability study, scalable problem, scalable discipline, diagonal-based, ...
Machine learning
Apply clustering, classification and regression methods from the machine learning community.
Features: clustering, classification, regression, quality measures, calibration, data transformation.
based on scikit-learn and OpenTURNS
Uncertainty
Define, propagate, analyze and manage uncertainties.
Features: distribution, uncertain space, empirical and parametric statistics, distribution fitting, sensitivity analysis, ...
based on OpenTURNS