Reliability-based Sampling for Model Calibration

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• Francisco Alejandro Diaz De la O (University of Liverpool)
• Monday 05 March 2018, 14:00-14:45
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

UNQW03 - Reducing dimensions and cost for UQ in complex systems

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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