Calibration is one of the most resource heavy tasks in engineering. Today, teams spend hours on expensive test rigs, running long cycles to capture every condition, and manually generating lookups to define system behavior. The result is wasted capacity, bottlenecks around equipment availability, and inconsistent outputs between programs.
At the same time, engineers are expected to deliver and get products to market faster. Traditional methods cannot keep up. Artificial intelligence offers a practical alternative by learning directly from existing test data.
With machine learning, engineers can identify high value tests, generate calibration maps, and replace costly sensors with accurate virtual equivalents. The impact is less time spent in the lab, faster decisions, and greater confidence in calibration results.
This whitepaper covers three core AI applications that leading OEMs, suppliers, and Monolith, now Physical AI from CoreWeave, have applied to calibration programs to improve and accelerate projects.
3 core applications of AI in calibration:
- Virtual sensor modeling: replace costly hardware with AI driven virtual sensors. Achieve ±6% accuracy across operating ranges using twenty times less test data.
- Test cycle design optimization: use Monolith's Next Test Recommender to prioritize only the most valuable drive cycles. Accelerate testing by a factor of twenty while still capturing critical behaviors.
- Calibration map prediction: generate accurate first pass maps with less data and fewer manual iterations. Standardize calibration outputs across platforms and programs.
