TopicM: Modelling and Simulation
Understanding and describing the mechanical behaviour and the damage evolution of materials used for highly-loaded components is a key requirement for a reliable use in general. In this context, a lot of research effort has been spent to develop and refine physical and mechanism based constitutive models in order to increase the preciseness of the underlying mechanical and damage predictions. In addition, probabilistic methods have been introduced into assessment concepts recently in order to describe both, the loading and the material scatter and thus the model input parameters by means of optimised probabilistic approaches. Lately, also pure data-driven approaches like Machine Learning come into play: on the one hand, to search for cross-correlations and to increase the understanding of the material; and on the other hand, to generate surrogate or so called grey-box models to train and provide very efficient tools for lifetime assessment in future.
Within this symposium, the role of mechanism based material models, innovative material sensor applications, novel parameter identification approaches, probabilistics and machine learning will be connect to each other and discussed together in order to summarize recent advances in the context of a future “Computer Aided Reliability in Mechanical Engineering”. The goal here is, to provide an interdisciplinary forum between material scientists, mechanical engineering experts, mathematicians and machine learning specialists to bring all these experts together at the MSE 2020 within this symposium or dedicated joint sessions respectively.