An active learning approach for improving the performance of equilibrium based chemical simulations
This work addresses efficiency issues for researchers in geoscience and chemistry by providing an incremental improvement in simulation methods.
The paper tackles the problem of reducing computational cost in equilibrium-based chemical simulations by proposing an active learning method that sequentially selects input data to build a surrogate model, resulting in a dramatic reduction in the number of function evaluations.
In this paper, we propose a novel sequential data-driven method for dealing with equilibrium based chemical simulations, which can be seen as a specific machine learning approach called active learning. The underlying idea of our approach is to consider the function to estimate as a sample of a Gaussian process which allows us to compute the global uncertainty on the function estimation. Thanks to this estimation and with almost no parameter to tune, the proposed method sequentially chooses the most relevant input data at which the function to estimate has to be evaluated to build a surrogate model. Hence, the number of evaluations of the function to estimate is dramatically limited. Our active learning method is validated through numerical experiments and applied to a complex chemical system commonly used in geoscience.