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.

翻译：我们开发了一种基于Transformer的序列到序列模型，该模型能够从单个解轨迹的不规则采样和含噪观测中，以符号形式恢复标量常微分方程(ODE)。通过大量实证评估，我们证明该模型在多种设置下的精确恢复性能优于或持平于现有方法。此外，我们的方法具有高效的可扩展性：在大量ODE集合上完成一次性预训练后，仅需通过模型的若干次前向传播即可推断出新观测解的支配律。