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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...
This paper presents a deep auto-encoder based model reduction method for large scale spatiotemporal process. This method includes three phases in order to find the near-optimal parameters of the reduced order model. The sequence of the phases is allocated according to the idea of greedy training which approximately minimizes the modeling error. This method also avoids including the spatial dimensionality...
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