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Surrogate modelling meets machine learning
(2019)Complex computational models are used nowadays in all fields of applied sciences to predict the behaviour of natural, economic and engineering systems. High-fidelity simulators are able to capture more and more realistic features by including multi-scale or multi-physics aspects in their governing equations, which can result in high complexity. Although computer power has attained unprecedented levels, it is still not possible to use brute ...Other Conference Item -
Combining machine learning and surrogate modeling for data-driven uncertainty propagation in high-dimension
(2019)Other Conference Item -
An adaptive algorithm based on spectral likelihood expansion for efficient Bayesian calibration
(2019)Other Conference Item -
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Dimensionality reduction and surrogate modelling for high-dimensional UQ problems
(2017)Other Conference Item -
Dimensionality reduction and surrogate modelling for high-dimensional UQ problems
(2017)Other Conference Item -
Combining feature mapping and Gaussian process modelling in the context of UQ
(2016)Other Conference Item -
Sparse polynomial chaos expansions as a machine learning regression technique
(2015)Other Conference Item