An iterative ensemble smoother (iES) based on regularized Levenburg-Marquardt, see the paper "Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem: Theory and Applications ", by Luo et al., SPE-176023-PA, https://doi.org/10.2118/176023-PA
This depository contains an PYTHON implementation of the aforementioned iES, which is most of the time used in ensemble-based reservoir data assimilation (also known as history matching) problems. Our main purpose here is to demonstrate how to use iES algorithm infer the input of a neural network(implemented with pytorch) . This code is based on lanhill/Iterative-Ensemble-Smoother: An iterative ensemble smoother (iES) based on regularized Levenburg-Marquardt which is implemented with MATLAB.
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The code may include mistakes, and can’t address every situation. If there is any question, we encourage you to do your own research, discuss with your community or contact us.
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