- Haeseong Cho, Jeonbuk National University
- Sang-Joon Shin, Seoul National University
During the last two decades, there exists rise and growth of data-driven methodology including proper orthogonal decomposition, deep learning, and machine learning in convergence to various fields of engineering. Such an innovative paradigm shift in computational engineering is crucial to realize and facilitate digital twins that mirror physical products and systems. Such a new digital engineering tool must enable to dealing with the computational complexities and integrate models and data from underlying physical phenomena. Regarding that, model order reduction provides powerful approaches to reduce the size of the computational models and allow obtaining accurate solutions in fast and efficient manner. It is based on the extraction of the relevant knowledge or feature from a set of available information (data) and using it for solving the original physical problem in a more compact form.
This mini-symposium aims to the development of novel computational methods related to model-order reduction for efficient and accurate co-simulation, as well as their implementation, with emphasis on the fields of structures, structural dynamics and aeroelasticity. Potential topics also include integrated modeling and design optimization, multiscale/multi-physics simulation based on the relevant data-driven process.