Calibrate complex systems faster for key performance requirements

Machine learning models don't replace your testing and data collection efforts, but they make it more efficient.  You start by training models with test data.  Using those models, you can predict performance characteristics under more operating conditions without having to run more tests.  So you can quickly get an idea of how your system performs in different scenarios.

If you need more data to make the best decision, you can again go back to the model for recommendations on which data will give you the most insight into your design.  Rather than guess at which additional tests you should run to learn more, tools like Next Test Recommender give you direction on the best tests needed to address unknowns in your design space.  When you apply machine learning to the problem, you can move much faster with even more confidence. 

Even the most experienced engineers can reinforce their knowledge with machine learning models.  When your working on a complex system, AI models can help you understand which inputs have the greatest influence on the outputs you are optimizing for. 

Balancing performance, efficiency, and safety in any calibration exercise starts with knowing exactly how your system operates and which inputs are most critical.  AI optimization tools can make tradeoffs across hundreds of options to find the best values for your system settings. Although machine learning models are too big to directly embed in controller processors, they help you get to the best option for system coefficient values faster and with more confidence.