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Review of surrogate modeling in water resources

Saman Razavi, Bryan A. Tolson, Donald H. Burn
2012 Water Resources Research  
Important observations and some guidance for surrogate modeling decisions are provided along with a list of important future research directions that would benefit the common sampling and search (optimization  ...  This paper reviews, analyzes, and categorizes research efforts on surrogate modeling and applications with an emphasis on the research accomplished in the water resources field.  ...  The authors would like to thank the reviewers, Holger Maier, Uwe Ehret, and the two anonymous reviewers, for their extensive and very helpful comments and suggestions, which significantly improved this  ... 
doi:10.1029/2011wr011527 fatcat:rvib7d3zhjei3olhldarvpyhpy

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, Berend Smit
2020 Chemical Reviews  
In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations.  ...  Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.  ...  Even if the literature is an article from the same group, reproducing the results from only a few years earlier can be a difficult search for the information that was not reported in the original article  ... 
doi:10.1021/acs.chemrev.0c00004 pmid:32520531 pmcid:PMC7453404 fatcat:l2745cqxl5fcnnwty73j2ckkyq

Adaptive surrogate models for parametric studies [article]

Jan N. Fuhg
2019 arXiv   pre-print
The Kriging framework with extension to multifidelity problems is presented and utilized to compare adaptive sampling techniques found in the literature for benchmark problems as well as applications for  ...  In this Master thesis adaptive sampling techniques are investigated for their use in creating metamodels with the Kriging technique, which interpolates values by a Gaussian process governed by prior covariances  ...  The NN is employed with 1 hidden-layer with 20 neurons and tanh activation function where Bayesian regularization is used as the training function.  ... 
arXiv:1905.05345v1 fatcat:trwrcwf3vvgprad4he5n7hd76u

Surrogate Models and Mixtures of Experts in Aerodynamic Performance Prediction for Mission Analysis

Rhea P. Liem, Joaquim Martins
2014 15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference   unpublished
with a trend).  ...  However, when we have a simple function profile, using a global model is more efficient than a mixture of experts, due to the added computational complexity in the latter.  ...  framework we use in this work.  ... 
doi:10.2514/6.2014-2301 fatcat:jz7g6qclj5ekddo2g7qpnmh4zy