The research group “Applied Mechanics and Data Analysis” (AMDA) is recently founded with a focus on building an interplay between the physics supported models and data analysis in a context of improved predictive analysis of engineering systems described by nonlinear material behaviour.
Incapability to extricate model parameter values or even a model form in some cases given experimental data, the absence of available data sets at sufficiently large space and/or time scales, and the never-ending issue of model validation are some of the main reasons for uncertainty quantification and data analysis of real-world phenomena. Today it is desired to quantitatively characterize and reduce uncertainties in both computational and real-world applications in either probabilistic or polymorphic forms. Therefore, proper methodologies and tools have to be developed in order to make such an analysis of real-scale structures possible.
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.
Collaborations with international research groups are embodied by participating in the research frameworks DFG SPP1748 Reliable Simulation Techniques in Solid Mechanics. Development of Non-standard Discretization Methods, Mechanical and Mathematical Analysis, DFG SPP 1886 Polymorphic uncertainty modelling for the numerical design of structure, the International Research and Training Group IRTG 1627 at Leibniz University Hanover, Germany, and Graduate school 2075. Modelling the constitutive evolution of building materials and structures with respect to aging at Technische Universität Braunschweig, Germany.