Prediction uncertainty from models and data

We present an approach to uncertainty propagation in dynamic systems, exploiting information provided by related experimental results along with their models. Our computational procedure draws from ideas and tools that are now common in robust control theory. A case study on a well-known database of methane combustion experiments and models demonstrates the viability of our proposed method.