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In this paper, we propose a novel sequential data-driven method for dealingwith equilibrium based chemical simulations, which can be seen as a specificmachine learning approach called active learning. The underlying idea of ourapproach is to consider the function to estimate as a sample of a Gaussianprocess which allows us to compute the global uncertainty on the functionestimation. Thanks to this estimation and with almost no parameter to tune, theproposed method sequentially chooses the most relevant input data at which thefunction to estimate has to be evaluated to build a surrogate model. Hence, thenumber of evaluations of the function to estimate is dramatically limited. Ouractive learning method is validated through numerical experiments and appliedto a complex chemical system commonly used in geoscience.

An active learning approach for improving the performance of equilibrium based chemical simulations