skip to content

Reliability-based Sampling for Model Calibration

Presented by: 
Francisco Alejandro Diaz De la O University of Liverpool
Monday 5th March 2018 - 14:00 to 14:45
INI Seminar Room 1

History Matching is a calibration technique that systematically reduces the input space in a numerical model. At every iteration, an implausibility measure discards combinations of input values that are unlikely to provide a match between model output and experimental observations. As the input space reduces, sampling becomes increasingly challenging due to the size of the relative volume of the non-implausible space and the fact that it can exhibit a complex, disconnected geometry. Since realistic numerical models are computationally expensive, surrogate models and dimensionality reduction are commonly employed. In this talk we will explore how Subset Simulation, a Markov chain Monte Carlo technique from engineering reliability analysis, can solve the sampling problem in History Matching. We will also explore alternative implausibility measures that can guide the selection of regions of the non-implausible in order to balance sampling exploration and exploitation.

The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons