At the Institute of Mechanics of Materials, a special focus is on the application of multi-scale methods to investigate the complex interactions in different materials. These methods enable us to simultaneously observe and model phenomena at different scales - from microscopic to macroscopic structures. Through high-resolution analysis, we can explicitly take local influences, such as phase and grain boundaries, into account and precisely predict their effects on the overall behavior of the material. This is particularly relevant for the development of new materials and technologies used in industrial and medical applications. Our research aims to develop innovative and resource-efficient solutions by combining detailed microscopic insights and macroscopic models.
The application of multiphysical problems plays an important role at the Instituter of Mechanics of Materials. These simulations enable us to simultaneously investigate complex interactions between different physical phenomena, such as thermomechanical, electrochemical and thermochemical processes. By integrating these different disciplines, we can develop a comprehensive understanding of material behavior under real operating conditions. For example, we research anodic dissolution and its transfer to polycrystals as well as the interactions during phase transformations in shape memory alloys. Our multiphysical approaches allow us to make precise predictions and develop innovative solutions for industrial and medical applications that are both efficient and resource-saving. This interdisciplinary approach promotes the development of new materials and technologies that meet the requirements of modern applications.
At the Institute of Mechanics of Materials, we increasingly rely on data-driven methods and model reduction techniques to manage the complexity of our simulations and increase the efficiency of our models. By using machine learning and artificial intelligence, we can analyze large amounts of data and recognize patterns that help improve our material models. These data-driven approaches enable us to make more precise predictions and increase the accuracy of our simulations. At the same time, we use model reduction techniques to minimize the computing time and resource requirements of our simulations without compromising accuracy. These techniques are particularly valuable when it comes to efficiently modeling complex multi-physical processes and multi-scale phenomena. By combining data-driven methods and model reduction techniques, we can develop innovative solutions that are both scientifically sound and practically applicable, thus making a significant contribution to material modeling.
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Last update: Dec 13, 2024