Abstract
Abstract. The computational costs associated with coupled reactive transport
simulations are mostly due to the chemical subsystem: replacing it
with a pre-trained statistical surrogate is a promising strategy to
achieve decisive speedups at the price of small accuracy losses and
thus to extend the scale of problems which can be handled. We
introduce a hierarchical coupling scheme in which “full-physics”
equation-based geochemical simulations are partially replaced by
surrogates. Errors in mass balance resulting from multivariate
surrogate predictions effectively assess the accuracy of
multivariate regressions at runtime: inaccurate surrogate
predictions are rejected and the more expensive equation-based
simulations are run instead. Gradient boosting regressors such as
XGBoost, not requiring data standardization and being able to handle
Tweedie distributions, proved to be a suitable emulator. Finally, we
devise a surrogate approach based on geochemical knowledge, which
overcomes the issue of robustness when encountering previously
unseen data and which can serve as a basis for further development of
hybrid physics–AI modelling.
Cited by
11 articles.
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