[2505.18996] Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs

Computer Science > Machine Learning arXiv:2505.18996 (cs) [Submitted on 25 May 2025 (v1), last revised 3 Mar 2026 (this version, v3)] Title:Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs Authors:Bob Junyi Zou, Lu Tian View a PDF of the paper titled Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs, by Bob Junyi Zou and 1 other authors View PDF HTML (experimental) Abstract:Hybrid neural ordinary differential equations (neural ODEs) integrate mechanistic models with neural ODEs, offering strong inductive bias and flexibility, and are particularly advantageous in data-scarce healthcare settings. However, excessive latent states and interactions from mechanistic models can lead to training inefficiency and over-fitting, limiting practical effectiveness of hybrid neural ODEs. In response, we propose a new hybrid pipeline for automatic state selection and structure optimization in mechanistic neural ODEs, combining domain-informed graph modifications with data-driven regularization to sparsify the model for improving predictive performance and stability while retaining mechanistic plausibility. Experiments on synthetic and real-world data show improved predictive performance and robustness with desired sparsity, establishing an effective solution for hybrid model reduction in healthcare applications. Comments: Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2505.18996 [cs.LG]   (or arXiv:2505.18996v3 [cs.LG] for ...