Главная
Study mode:
on
1
Intro
2
Outline
3
Problem setting
4
GP emulation
5
Sequential adaptive designs
6
ALM and ALC
7
Greedy mutual information criterion
8
MI sequential design algorithm
9
Designing sensor placements (Krause et al., 2008)
10
A practical issue with the MI algorithm
11
Mutual Information for Computer Experiments (MICE)
12
A nugget parameter for smoothing
13
The improvement in terms of robustness
14
A visualisation of the design selection
15
A comparison of the computational cost
16
Numerical results: 4-D Oscillatory Function
17
Numerical results: 7-D Piston Simulation
18
Case study tsunami modelling
19
An extension: MICE in GP optimisation (optim-MICE)
Description:
Explore sequential design based on mutual information for computer experiments in this 20-minute talk by Joakim Beck from KAUST. Learn about uncertainty quantification, Gaussian Process emulation, and adaptive design strategies for complex physical systems. Discover the Mutual Information for Computer Experiments (MICE) algorithm and its applications in sensor placement, oscillatory function modeling, piston simulation, and tsunami modeling. Gain insights into practical issues, computational costs, and extensions to GP optimization.

Sequential Design Based on Mutual Information for Computer Experiments - Joakim Beck, KAUST

Alan Turing Institute
Add to list
#Mathematics #Statistics & Probability #Uncertainty Quantification #Numerical Analysis #Science #Earth Science #Climate Science #Climate Modeling #Computer Science #Information Theory #Mutual Information
0:00 / 0:00
1 of 19

Intro