论文标题
动态系统建模的深度表示学习
Deep Representation Learning for Dynamical Systems Modeling
论文作者
论文摘要
正确的状态表示是混乱系统成功动态建模的关键。受自然语言处理和计算机视觉等各个领域的深层表示的最新进展的启发，我们提出了对最先进的变压器模型的适应，以应用到动态系统建模。该模型在轨迹产生以及一般吸引子的特征近似（包括状态的分布和Lyapunov指数）中表明了有希望的结果。
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.
