Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs

• URL: http://arxiv.org/abs/2505.18996v1
• Date: Sun, 25 May 2025 06:36:30 GMT
• Title: Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs
• Authors: Bob Junyi Zou, Lu Tian,
• Abstract summary: We propose a new pipeline for automatic state selection and structure optimization in mechanistic neural ODEs.<n>Experiments on synthetic and real-world data show improved predictive performance and robustness with desired sparsity.
• Score: 7.240170769827935
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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