Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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

Abstract : 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.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal-agroparistech.archives-ouvertes.fr/hal-03396819
Contributor : Céline Lévy-Leduc Connect in order to contact the contributor
Submitted on : Friday, October 22, 2021 - 5:44:15 PM
Last modification on : Wednesday, December 1, 2021 - 2:49:27 PM

Links full text

Identifiers

  • HAL Id : hal-03396819, version 1
  • ARXIV : 2110.08111

Citation

Mary Savino, Céline Lévy-Leduc, Marc Leconte, Benoit Cochepin. An active learning approach for improving the performance of equilibrium based chemical simulations. 2021. ⟨hal-03396819⟩

Share

Metrics

Record views

48