Author

O. Patil

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Preprint Oct 2026

Adaptive Control as an Information Gradient Flow: Excitation, Constraints, Energy, and Stochastic Learning

Adaptive control is usually developed through Lyapunov stability, excitation, and parameter convergence, whereas neighboring fields describe learning using convexity, information geometry, variational principles, and stochastic thermodynamics. This paper develops a constrained common language for these viewpoints. For...

O. Patil · 0 citations

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