Modelling of metal forming processes is an essential task of production engineering. Due to
the latest technological developments, a huge variety of models is already available and …

We present a novel active learning algorithm, termed as iterative surrogate model
optimization (ISMO), for robust and efficient numerical approximation of PDE constrained …

We propose a supervised deep learning algorithm based on low-discrepancy sequences as
the training set. By a combination of theoretical arguments and extensive numerical …

Scramjet is a promising propulsion technology that provides efficient and flexible access-to-
space and high-speed point-to-point transportation. Since the design process of scramjet …

航空发动机全生命周期过程中存在着大量的随机和认知不确定性因素, 往往带来研发迭代周期长
, 制造合格率低, 使用维护困难等一系列问题. 近年来国内外针对不确定性分析已经开展了一系列 …

In the last few decades, uncertainty quantification (UQ) methods have been used widely to
ensure the robustness of engineering designs. This chapter aims to detail recent advances …

We propose a multi-level method to increase the accuracy of machine learning algorithms
for approximating observables in scientific computing, particularly those that arise in systems …

A methodology to estimate the fatigue life of an anisotropic structure under uncertainty is
established in this work for the first time, utilizing the statistical inference of the Equivalent …

We consider the problem of chance constrained optimization where it is sought to optimize a
function and satisfy constraints, both of which are affected by uncertainties. The real world …

A compressed sensing method based on polynomial chaos expansion (CS-PCE) is used to
study separation control over an airfoil with a synthetic jet actuator (SJA). Uncertainty …