Optimize parameters in physics based models quickly and efficiently
The focus of this webinar is to show the basics of efficient optimization of parameters in physics based models such as those used in the ECU and simulation environment. We will explain how the simple and intuitive operation of the ETAS ASCMO-MOCA tool can be used to efficiently perform even complex optimization tasks.
Powerful algorithms are able to optimize a large number of free parameters simultaneously, taking into account constraints such as smoothness or monotonicity.
ASCMO-MOCA is often used to optimize the predictive quality of ECU models (virtual sensors) for e. g. torque or exhaust gas temperature. This means that the deviation of the model prediction compared to a real measurement from the engine test bench or the vehicle for all measurement points is minimized.
Since the methodology used in ASCMO-MOCA is universally applicable, applications can be found not only in the environment of combustion engines, but also in areas such as e-mobility (e. g. charging strategy) and component development.
In this webinar you will learn:
- How the tool's simple and intuitive operation allows even complex optimization tasks to be performed efficiently
- Which common model and data formats can be used
- How different tasks can be solved in various ways due to the ability of freely reproducing functions as formulas
- How interactive graphical representations of data and results facilitate interpretation and evaluation
- How a result can be determined quickly through powerful optimization methods
Senior Product Manager
Data-based modeling and model-based calibration