AI & ML
Koopman Generator Decomposition for Port-Hamiltonian Systems
arXiv eess.SY0 views
arXiv:2603.17849v3 Announce Type: replace Abstract: Port-Hamiltonian energy balance and Koopman approximation describe different aspects of nonlinear dynamics. This paper distinguishes exact observable identities, measure-dependent Galerkin projections, constrained surrogate identification, and certification of a controller on the nonlinear plant. Conservative weak skewness requires compatible measure and boundary conditions. Dissipative positivity belongs to the metric bracket; it does not generally make the projected first-order dissipative derivation positive. We give an explicit fixed-metric compatibility condition and a constant--Hamiltonian dictionary obstruction, and state the assumptions under which surrogate certificates imply plant bounds. A reproduced pendulum experiment illustrates the practical gap: its learned controller admits an exact shaped-energy certificate on the nonlinear plant, although its fitted quadratic storage is not a plant Lyapunov function and its archived residual-norm diagnostic is uninformative. These conclusions use the specified plant structure; they are not generic finite-data guarantees. The bracket, projection, and Lyapunov arguments are positioned relative to existing structured identification and residual-aware control methods.
Source:https://arxiv.org/abs/2603.17849PreviousA System-of-Systems Convergence Paradigm for Societal Challenges of the AnthropoceneNextOn-Line Policy Iteration with Trajectory-Driven Policy Generation
Related posts
Meta Quest Promo Codes and Coupons for November 2026
0Wired
BullMQ in NestJS: Retries, Idempotency and Dead-Letter Queues
0dev.to
80.6% at Chores, 12% at Real Work: AI's Benchmark Week
0dev.to
Efficient estimation of regression models for recurrent events in presence of terminal events
0arXiv stat.CO
Local Whittle estimation for distributional time series: A regularization framework in Bayes Hilbert space
0arXiv stat.CO
Inventory and Assortment Optimization under General Choice: A Unified Approximation Framework
0arXiv stat.CO