Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting
从连续时间动力学模型中挖掘因果结构：海啸预测应用
Continuous-time dynamics models, such as neural ordinary differential equations, have enabled the modeling of underlying dynamics in time-series data and accurate forecasting. However, parameterization of dynamics using a neural network makes it difficult for humans to identify causal structures in the data. In consequence, this opaqueness hinders the use of these models in the domains where capturing causal relationships carries the same importance as accurate predictions,…