Dymoval (Dynamic Model Validation)

What is it?

Dymoval is a Python package for analyzing measurement data and validating models.

Dymoval validates models based only on measurements and simulated data, and it is completely independent of the used modeling tool. This means that it does not matter if a model has been developed with Simulink, Modelica, etc., dymoval will only look at the produced data from the model.

If you are tracking your models changes in a CI/CD environment, then dymoval API can be easily used to run tests in Jenkins or GitHub Actions pipelines as it enables unit-testing on models.

Finally, dymoval provides a number of functions for for handling measurements data, addressing common issues such as noise, missing data, and varying sampling intervals.

What is not.

Dymoval is not a tool for developing models. You have to develop your models with the tool you prefer.

It is not a tool for System Identification either (but we don’t exclude it can happen in the future ;-)).

Dymoval only checks if your models are good or not but you have to develop your models by yourself in the environment that you prefer.

Why dymoval?

Simulation results frequently deviate significantly from real-world measurements, leading to a growing skepticism towards simulation models. Dymoval is dedicated to bridging this gap, aiming to restore confidence in simulation accuracy and reliability.

Dymoval specializes in model validation, offering robust solutions for a variety of models, including MIMO (Multiple Input Multiple Output) and stiff models, all in an easy and comprehensible manner. Additionally, Dymoval provides a comprehensive toolbox designed to handle real-world measurement data, which often comes with challenges such as noise, missing data, and varying sampling intervals. This ensures that your models are not only validated but also capable of accurately reflecting real-world conditions.

Main Features

Measurements analysis and manipulation

  • Time and frequency analysis

  • Easy plotting

  • Missing data handling

  • Linear filtering

  • Means and offsets removal

  • Re-sampling

  • Physical units

Model validation

  • Validation metrics:

  • R-square fit

  • Residuals auto-correlation statistics

  • Input-Residuals cross-correlation statistics

  • Coverage region

  • MIMO models

  • Independence of the modeling tool used.

  • API suitable for model unit-tests

Index

Indices and tables