Deep Representation Learning for Dynamical Systems Modeling

10 Feb 2020 · Anna Shalova, Ivan Oseledets ·

Proper states' representations are the key to the successful dynamics modeling of chaotic systems. Inspired by recent advances of deep representations in various areas such as natural language processing and computer vision, we propose the adaptation of the state-of-art Transformer model in application to the dynamical systems modeling. The model demonstrates promising results in trajectories generation as well as in the general attractors' characteristics approximation, including states' distribution and Lyapunov exponent.

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Representation Learning

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Absolute Position Encodings • Adam • BPE • Dense Connections • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • ReLU • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer

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Deep Representation Learning for Dynamical Systems Modeling

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