This paper addresses the training of Neural Ordinary Differential Equations (neural ODEs), and in particular explores the interplay between numerical integration techniques, stability regions, step size, and initialization techniques. It is shown how the choice of integration technique implicitly regularizes the learned model, and how the solver's corresponding stability region affects training and prediction performance. From this analysis, a stability-informed parameter initialization technique is introduced. The effectiveness of the initialization method is displayed across several learning benchmarks and industrial applications.

翻译：本文研究神经常微分方程（Neural ODEs）的训练过程，重点探讨数值积分技术、稳定性区域、步长与初始化方法之间的相互作用。我们揭示了积分技术选择如何隐式正则化所学模型，以及求解器对应的稳定性区域如何影响训练与预测性能。基于该分析，提出了一种稳定性感知的参数初始化技术。该初始化方法在多个学习基准测试与工业应用中均展现出优越性能。