Robust system design with limited experimental data and an inexact simulation model. (English) Zbl 1542.68202
Summary: Computer simulations of physical systems are commonly used to improve system performance via optimizing the system design. We study the case when, in addition to the simulation, a limited amount of experimental data is collected from the real physical system. This article describes a method for selecting a conservative system design that is robust to uncertainty from the simulation parameters and simulation bias. The concept is that each potential system design is assigned a worst-case scenario in a data-driven feasible region. The conservative system design is then chosen as the best of the worst-cases. The method is shown to have good statistical properties. A case study is performed where a vehicle safety belt design is chosen to minimize the impact of vehicle crashes on a driver.
MSC:
| 68U07 | Computer science aspects of computer-aided design |
| 62G08 | Nonparametric regression and quantile regression |
| 62G15 | Nonparametric tolerance and confidence regions |
| 82-10 | Mathematical modeling or simulation for problems pertaining to statistical mechanics |
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