No abstract

Thanks to their versatility, ease of deployment and high-performance, surrogate models have become staple tools in the arsenal of uncertainty quantification (UQ). From local interpolants to global spectral decompositions, surrogates are characterised by their ability to efficiently emulate complex computational models based on a small set of model runs used for training. An inherent limitation of many surrogate models is their susceptibility to the curse of dimensionality, which traditionally limits their applicability to a maximum of O(10 2 ) input dimensions. We present a novel approach at high-dimensional surrogate modelling that is model-, dimensionality reduction-and surrogate model-agnostic (black box), and can enable the solution of high dimensional (i.e. up to O(10 4 )) problems. After introducing the general algorithm, we demonstrate its performance by combining Kriging and polynomial chaos expansions surrogates and kernel principal component analysis. In particular, we compare the generalisation performance that the resulting surrogates achieve to the classical sequential application of dimensionality reduction followed by surrogate modelling on several benchmark applications, comprising an analytical function and two engineering applications of increasing dimensionality and complexity.

No abstract

General-purpose uncertainty quantification software has become a well established requirement in modern engineering workflows. Different communities (e.g. applied maths, engineering, economics, etc.), however, generally employ diverse arrays of technologies and workflows, from computing infrastructure to programming languages. To overcome the intrinsic limitation of a single language, standalone software package, we introduce UQCLOUD, an OS-and programming language-independent, cloud-based version of UQLAB. UQCLOUD follows a software-as-a-service (SaaS) model, that allows anyone to take advantage of the wellestablished UQLAB suite, without the need to adapt their computational workflows to include MATLAB. UNCECOMP 2021 4 th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering M. Papadrakakis, V. Papadopoulos, G. Stefanou (eds.

No abstract