효율적인 동적 시스템 예측을 위한 데이터 중심 축소모델 연구
Data-driven reduced-order model for efficient prediction of structural dynamics Haeseong Cho Department of Aerospace Engineering, Jeonbuk National University, Korea With the advent of the 4th industrial revolution, the next-generation mechanical and aerospace industries require multi-disciplinary convergence technology, and multi-disciplinary simulations are drawing attention as an important tool for the design and operation of next-generation mechanical and aerospace systems. Recently, a data-driven model reduction method using data analysis or machine learning, is emerging, and is expected as a base technology for a digital twin for a complex multi-disciplinary system. In general, the methods that can be used to define the reduced-order model of a dynamic system are the intrusive model reduction method, which projects the governing equations of the system in a generalized coordinate system, and the non-intrusive model reduction method, which defines the input and output relationship of the data of interest. In this talk, the model reduction methods that defines a reduced model using the solution of a dynamic system such as the displacement of a structure will be introduced....