Reduce model complexity for faster simulation

Model Order Reduction

Find out more about this professional toolbox

Train models on simulation data

The Proper Orthogonal Decomposition method used is a non-intrusive way of building a reduced model through simulation data. To assess a system behavior depending on certain set of parameters, you can infer a reduced model by training it on specific simulation datasets.

Learn more about Proper Orthogonal Decomposition.

Run fast design space exploration

You can then leverage your reduced model for fast and accurate simulation around a given operating point. This will allow you, for instance, to perform design space exploration for a wide set of parameters, including geometry changes. With those results you will then be able to perform design optimization.

Find out more about the implementation of the Scilab Model Reduction Toolbox.

Deploy beyond simulation experts

Thanks to model order reduction, you can now deploy models beyond simulation specialists to the engineering and manufacturing departments, and following the product in-service. Reduced models will allow you to create a new simulation experience through the coupling of system models with finite element models, but also multi-scale and multi-physics approaches.

Find out more how to leverage model reduction for real-time control.

Based on methods of variable separation

In this first version, the model order reduction is performed through the separation of space (x) and time (t), thanks to the so called method of “Proper Orthogonal Decomposition”. This leverages the matrix computation capabilities of Scilab, such as Singular Value Decomposition

Parameter evaluation

Time evaluation

Features V1.0

  • Proper Orthogonal Decomposition
  • Mesh/simulation data import
  • Steady/Unsteady cases
  • 1D/2D/3D cases
  • Mesh morphing
  • Classical/Snapshot methods
  • Multiparametric prediction
  • Monoparametric visualization
    (through ESI Player)