Deep Learning-Based Model Reduction for Distributed Parameter Systems
提出一种基于深度自编码器的模型降阶方法，将分布参数系统的时空数据压缩为低维表示并重建，实验表明比传统本征正交分解方法更准确高效。
This paper presents a deep learning-based model reduction method for distributed parameter systems (DPSs). The proposed method includes three phases. In phase I, numerical or experimental data of the spatiotemporal distribution is reduced into low-dimensional representations using the deep auto-encoder (DAE). In phase II, the low-dimensional representations are used to establish the reduced-order model. In phase III, the reduced model is then used to reconstruct the high-dimensional DPS. Experimental studies are conducted to validate the proposed method. The proposed method is compared with the classical proper orthogonal decomposition method and demonstrates better modeling accuracy and efficiency in the experiments.