Uncertainty Quantification


With the recent advancement of uncertainty quantification techniques and machine/deep learning, the research group is focusing on incorporating stochastic descriptions into applied mechanics problems. In particular special attention is paid to the analysis of the mechanics of materials and systems, for small and large deformations, in quasi-static and dynamic conditions, as well as to the multiscale modelling. The research is based on an interdisciplinary approach combining experiments, mathematical modelling and numerical approaches to the quantification of uncertainty, its prediction and data assimilation. The main goal is to develop efficient and robust learning and uncertainty quantification numerical algorithms of wide range purposes that can be used for solving practical problems starting from material aging up to the design of controllers for manufacturing processes. Having in mind that the real applications are often time-dependent and of a large-scale and nonlinear nature, the ongoing research is also trying to address this problem.