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Engineering-Driven Statistical Adjustment and Calibration

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If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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