COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Experimental Design for Prediction of Physical System Means Using Calibrated Computer Simulators

## Experimental Design for Prediction of Physical System Means Using Calibrated Computer SimulatorsAdd to your list(s) Download to your calendar using vCal - Angela Dean (Ohio State University)
- Wednesday 11 April 2018, 09:00-10:00
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact INI IT. UNQW04 - UQ for inverse problems in complex systems Computer experiments using deterministic simulators are often used to supplement physical system experiments. A common problem is that a computer simulator may provide biased output for the physical process due to the simplified physics or biology used in the mathematical model. However, when physical observations are available, it may be possible to use these data to align the simulator output to be close to the true mean response by constructing a bias-corrected predictor (a process called calibration). This talk looks at two aspects of experimental design for prediction of physical system means using a Bayesian calibrated predictor. First, the empirical prediction accuracy over the output space of several different types of combined physical and simulator designs is discussed. In particular, designs constructed using the integrated mean squared prediction error seem to perform well. Secondly, a sequential design methodology for optimizing a physical manufacturing process when there are multiple, competing product objectives is described. The goal is to identify a set of manufacturing conditions each of which leads to outputs on the Pareto Front of the product objectives, i.e. identify manufacturing conditions which cannot be modified to improve all the product objectives simultaneously. A sequential design methodology which maximizes the posterior expected minimax fitness function is used to add data from either the simulator or the manufacturing process. The method is illustrated with an example from an injection molding study. The presentation is based on joint work with Thomas Santner, Erin Leatherman, and Po-Hsu Allen Chen. This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
- Featured lists
- INI info aggregator
- Isaac Newton Institute Seminar Series
- School of Physical Sciences
- Seminar Room 1, Newton Institute
- bld31
Note that ex-directory lists are not shown. |
## Other listsShort Course: Higher order regularisation in imaging 'Women in Medicine' - Cambridge MedSoc Talks## Other talksCelebration of Ten Years in Partnership with Nazarbayev Intellectual Schools BSU Seminar: “Cox-process representation and inference for stochastic reaction-diffusion processes” Graphons and Graphexes as Limits of Sparse Graphs: Part I MicroRNAs as circulating biomarkers in cancer The St Petersburg Paradox and other Puzzles From The Past |