Mutual Information for Computer Experiments (MICE): design, optimization, and data assimilation: applications to tsunami hazard

Serge Guillas (University College London, UK)

We present a new method for the design of computer experiments. The sequential design algorithm MICE (Mutual Information for Computer Experiments) adaptively selects the input values at which to run the computer simulator, in order to maximize the expected information gain (mutual information) over the input space. The superior computational efficiency of MICE compared to other algorithms is demonstrated on test functions, and on the tsunami model VOLNA with overall gains of 20-50%. Moreover, there is a clear computational advantage in building a design of computer experiments solely on a subset of active variables. However, this prior selection inflates the limited computational budget. We thus interweave MICE with a screening algorithm to improve the overall efficiency of building an emulator. This approach allows us to assess future tsunami risk for complex earthquake sources over Cascadia. An application to optimization of expensive black-box functions using MICE is also introduced. It is then employed in a data assimilation scheme to design an optimal network of buoys near shore for the purpose of detecting incoming tsunamis.

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