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physics.comp-ph2022★ 1 cited
Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations
Benjamin Wu, Oliver Hennigh, Jan Kautz +2
Physics-informed neural networks allow models to be trained by physical laws described by general nonlinear partial differential equations. However, traditional architectures strug…
physics.comp-ph2018
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
Pantelis R. Vlachas, Wonmin Byeon, Zhong Y. Wan +2
We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks…