Commit e3e91c4f authored by Janis Jatnieks's avatar Janis Jatnieks
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Update README.md

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......@@ -5,7 +5,10 @@ for reaplcing a slow running simulator. This code was written for a reactive tra
coupled to a geochemistry simulator (reactions in time and space) to simulate scenarios such as underground storage of CO2 or
hydrogen storage for excess energy from wind farms. The challenge for such applications is that the geochemistry simulator is
typically slow compared to fluid dynamics and constitutes the main bottleneck for producing highly detailed simulations of
such application scenarios. This auto-ML approach attempts to find machine learning models that can replace the slow running simulator when trained on input-output data from the geochemistry simulator. The code may be of more general interest as this prototype can be used to screen many different machine learning models for any regression problem in general. It also contains a demonstration example using the Boston housing standard data-set.
such application scenarios. This approach attempts to find machine learning models that can replace the slow running simulator
when trained on input-output data from the geochemistry simulator. The code may be of a more general interest as this prototype
can be used to screen many different machine learning models for any regression problem in general.
To illustrate this it also includes a demonstration example using the Boston housing standard data-set.
# Materials
For the proof-of-concept research pioneering the use of surrogate models for reactive transport geochemistry, see our [paper here](https://www.sciencedirect.com/science/article/pii/S1876610216310050) and our [EGU Poster presentation summarizing this work here](https://presentations.copernicus.org/EGU2016-12923_presentation.pdf).
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