Author

O. Patil

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Preprint Oct 2026

Nesterov-Accelerated Concurrent Learning for Lyapunov-Based Deep Neural Networks

Concurrent learning (CL) has recently been extended to Lyapunov-based deep neural networks (DNNs) to obtain parameter convergence under finite rather than persistent excitation. However, the existing first-order law converges slowly, especially for high-dimensional systems and overparameterized networks. To address thi...

Hossein Papi, O. Patil · 0 citations

#machine learning Preprint Sep 2026

Neural ODEs Meet Concurrent Learning: Stable Online Learning with Lyapunov Guarantees

Neural ODEs learn dynamics from trajectory losses, but their adjoint gradients lack the regressor-times-parameter-error structure on which Lyapunov analyses of online adaptation rest, so training on streaming data comes without stability guarantees. We show that this structure is in fact present: the adjoint gradient d...

O. Patil · 0 citations