A new paper with the title “Multilevel surrogate modeling approach for optimization problems with polymorphic uncertain parameters” is published in the International Journal of Approximate Reasoning. The paper is part of the Special Issue Reliable Computing and contains a surrogate modeling strategy, where deterministic finite element simulations, stochastic analyses as well as interval analyses are approximated by sequentially trained artificial neural networks.

The paper can be downloaded here: http://dx.doi.org/10.1016/j.ijar.2019.12.015.

Free download is available until March 3, 2020 via https://authors.elsevier.com/a/1aOcE,KD6ZNmqq