Equilibrium-based models are a transparent method of modelling shoreline change, though often too simplistic to capture complex dynamics. Conversely, deep learning methodologies offer greater predictive power at the expense of transparency. In this research we scrutinize the internal workings of an LSTM shoreline model. A regression-based probe is used to show that cell state vectors, responsible for past-to-future information flow, autonomously generate equilibrium-like information akin to the physics-based equilibrium term of the ShoreFor model, Ωeq. The variation in probe skill throughout training is tracked to show that at 5 of 6 transects, the LSTM was able to meaningfully acquire equilibrium information (ΣΔR2 = 0.3–0.6). The results of this work offer evidence that an LSTM may model shoreline change with internal methods that are consistent with the current understanding of coastal shoreline dynamics. These physically meaningful representations emphasize the importance of co-evolution between machine learning and physics-based approaches moving forward.

中文翻译：

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LSTM 内存状态是否反映了降低复杂性的沙质海岸线模型中的关系


基于平衡的模型是一种透明的海岸线变化建模方法，尽管通常过于简单而无法捕捉复杂的动态。相反，深度学习方法以牺牲透明度为代价提供了更强的预测能力。在这项研究中，我们仔细研究了 LSTM 海岸线模型的内部工作原理。基于回归的探针用于表明，负责过去到未来信息流的单元状态向量会自主生成类似平衡的信息，类似于 ShoreFor 模型的基于物理的平衡项 Ωeq。跟踪整个训练过程中探针技能的变化，以表明在 6 个样带中的 5 个样带上，LSTM 能够有意义地获取平衡信息 （ΣΔR2 = 0.3–0.6）。这项工作的结果提供了证据，表明 LSTM 可以使用与当前对沿海海岸线动力学的理解一致的内部方法对海岸线变化进行建模。这些具有物理意义的表示强调了机器学习和基于物理的方法之间协同进化的重要性。