2:00PM - Maury 115
Robust Experimental Designs for Model Calibration
Many scientific and engineering problems in industry are explored with the aid of a physics-based model (or computer simulation). Real physical data is often collected in order validate or calibrate the physics-based model. These models can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration parameters can be estimated from real data by conducting physical experiments. This talk presents an approach to optimally design such a physical experiment. The problem of optimally designing a physical experiment, using a physics-based model, is similar to the problem of finding an optimal design for nonlinear models. However, the problem is more challenging than the existing work on nonlinear optimal design because of the possibility of model discrepancy, that is, the physics-based model may not be an accurate representation of the true physical system. Therefore, we propose an optimal design approach that is robust to potential model biases. We show that our designs are better than the commonly used physical experimental designs that do not make use of the information contained in the physics-based model and other nonlinear optimal designs that ignore potential model biases. We illustrate our approach using a toy example and a real example from Procter & Gamble.