ENBIS-16 in Sheffield11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016
Pre-Conference Workshop on "Introduction to Kriging using R and JMP" by Nicolas Durrande and Volker Kraft11 September 2016, 09:00 – 12:00
This full-day ENBIS-16 pre-conference workshop will be given by Nicolas Durrande (France) and Franco Pavese (Italy), both from IMEKO TC21. It can be booked here.
Nicolas Durrande is a lecturer at Mines St-Etienne (France). His research interests include Gaussian process models and kernel theory. He is co-organizer of the OQUAIDO project (Chair of applied mathematics at Mines St-Etienne), which comprises thirteen industrial and academic partners working jointly on the topic of computer experiments.
Volker Kraft is the Academic Ambassador for JMP in Europe, fostering and supporting the use of JMP in teaching and research. He holds a Ph.D. in Electrical Engineering (University Bochum, Germany), and used statistical methods extensively in his research in psychoacoustics and speech communication.
People with applications that require them to work with functions, often of many variables, that are costly to evaluate. Knowledge of linear regression, statistical modeling, and stochastic processes is helpful but is not required for the workshop. Similarly, basic knowledge of R and/or JMP will be helpful for the hands-on lab component, but is not mandatory.
Kriging (or Gaussian process regression) has proven to be of great interest when trying to approximate a costly to evaluate function in a closed form. The principle aim of the workshop is to show how to build useful surrogate models using this approach, and to make clear the assumptions that such models rely on. Furthermore, once it exists, we will show how a surrogate model can be used for optimization. In the morning we introduce the concepts of kriging through lectures and demonstrations. During the afternoon we apply some of the key methods in lab sessions using both R and JMP. The main aim of the lab is to quickly find optimal settings of a catapult numerical simulator that can fire the longest shot.
TEACHING OBJECTIVES & LEARNING OUTCOMES:
- Understand how to build and validate Kriging models
- Understand the assumptions behind Kriging models
- Appreciate the influence of the parameters in Kriging models
- Gain experience of designing and analyzing computer experiments in R and JMP
Morning: An Introduction to Kriging
- Surrogate models (or emulators) in engineering
- Kriging (Gaussian process regression)
- Covariance functions
- Parameter estimation
- Model validation
- Application to optimization
- Design of experiments