Engineering-Driven Statistical Adjustment and Calibration
Seminar Room 1, Newton Institute
There can be discrepancy between physics-based models and reality, which can be reduced by statistically adjusting and calibrating the models using real data. Gaussian process models are commonly used for capturing the bias between the physics-based model and the truth. Although this is a powerful approach, the resulting adjustment can be quite complex and physically non-interpretable. A different approach is proposed here which is to postulate adjustment models based on the engineering judgment of the observed discrepancy. This often leads to models that are very simple and easy to interpret. The approach will be illustrated using many real case studies.