High-order expansion of neural ordinary differential equation flows.

ABSTRACT: Artificial neural networks, widely recognized for their role in machine learning, are also transforming the study of ordinary differential equations (ODEs), bridging data-driven modeling with classical dynamical systems as well as enabling the development of infinitely deep neural models. However, their practical applicability remains, in this context, constrained by the opacity of the learned dynamics, which operate as black-box systems with limited explainability, thereby hindering trust in their deployment. Existing approaches for the analysis of neurally driven dynamical systems are now scarce and restricted to first-order gradient information due to computational constraints, thereby limiting the depth of achievable insight. Here, we introduce event transition tensors as a new tool co

SUBMITTER: Izzo D 

PROVIDER: S-EPMC12710715 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature