We develop a transformer-based sequence-to-sequence model that recovers
scalar ordinary differential equations (ODEs) in symbolic form from irregularly
sampled and noisy observations of a single solution trajectory. We demonstrate
in extensive empirical evaluations that our model performs better or on par
with existing methods in terms of accurate recovery across various settings.
Moreover, our method is efficiently scalable: after one-time pretraining on a
large set of ODEs, we can infer the governing law of a new observed solution in
a few forward passes of the model.

我们使用基于 Transformer 的序列到序列模型，从单个解轨迹的不规则采样和嘈杂观测数据中恢复标量常微分方程（ODE）的符号形式。通过广泛的实证评估，我们证明我们的模型在各种环境下表现出更好或与现有方法相当的精确恢复能力。此外，我们的方法具有高效的可扩展性：在一次对大量 ODE 进行预先训练后，我们可以在模型的几次正向传递中推断出新观测解的控制规律。