Progress Towards Gaussian Process Emulation for Improving Multiphase EoS Optimisation

Multiphase equation of state (EoS) uncertainty quantification (UQ) is limited by the dimensionality of the EoS input vector, the EoS model calculation time, and phase boundaries that are sensitive to input parameter changes. For multiphase materials the number of input parameters is high which makes the optimisation space large. Therefore, optimisation of EoS parameters takes many EoS calculations to complete. Gaussian Process Emulation (GPE) with Bayesian Optimisation (B-Opt) provides an efficient methodology for mapping the multiphase EoSs cost function. Confidence set contours from GPE with B-Opt applied to single phases of Tin are utilised to aid the optimisation procedure for a multiphase EoS model. The confidence set contours alter the optimisation procedure by: providing non-random starting locations, and creating a non-euclidean optimisation space. This reduces the total number of multiphase EoS calculations required to find the optimum answer.

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