Commit d360867a authored by Janis Jatnieks's avatar Janis Jatnieks
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Update README.md

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# Intro
Surrogate playground is an automated machine learning
framework (a kind of auto ML) written for rapidly
screening a large number of different ML models
models to serve as surrogates for a slow runnig simulator.
to serve as surrogates for a slow runnig simulator.
This was written for a reactive transport application where
a fluid flow model (hydrodynamics) is coupled to a
......@@ -13,8 +14,30 @@ geochemistry simulators are quite slow copared to fluid
dynamics and constitute the main bottleneck for producing
highly detaled simulations of such application scenarios.
# Quick start
While this project was written for a very specific application
of surrogate models in mind, it can be re-used for more general
prupose ML model creation. See the file ending with experiment_controller.R
for a basic example of set-up.
prupose ML model creation. See the file example files to get an
idea of how to quickly launch a large number of ML model fitting
experiments:
* `coupled_experiment_controller.R` for an example of how to use
this for surrogate model fitting the way it was designed to be used
* `general_experiment_controller.R` for a general purpose ML fitting
approcah example
The key difference between coupled surrogat models and the gen
purpose examples are that for couped simulation experiemtns there are
sometimes special fields and filtering considerations that you can
use for the fitting experiements. Among them is the
* ability to ensure that concentration values can never be negative as some models
incorrectly output slighly negative values, but this is not possible
in the real world
* that some fields that are expected by the ML model as input,
will be supplied at run-time from the hydrofynamics or thermal
model, but are not produced as outputs form the surrogate
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