The paper introduces a Bayesian method for jointly estimating Linear Parameter-Varying (LPV) state-space models and their scheduling maps from input-output data, while explicitly quantifying both aleatoric and epistemic uncertainties. This approach preserves the LPV structure necessary for controller synthesis and provides confidence bounds on predicted responses, enabling efficient simulation and uncertainty propagation. The method is illustrated on a surrogate model of a two-dimensional nonlinear mass‑spring‑damper system.
arXiv:2604. 21030v2 Announce Type: replace-cross Abstract: The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making and adaptive control.
arXiv:2606. 13794v1 Announce Type: cross Abstract: Nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind conventional, linear control allocation techniques.
arXiv:2609.36712v1 Announce Type: cross Abstract: Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this chall...
Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this challenge for stochastic controlled nonlinear dynamical...
The paper introduces a robust and adaptive model predictive control framework for uncertain nonlinear systems with bounded disturbances and unmodeled nonlinearities, leveraging Gaussian Processes to learn dynamics from noisy measurements. It derives robust predictions for GP models using contraction metrics, integrating them into the MPC formulation to ensure recursive feasibility, robust constraint satisfaction, and convergence to a reference state with high probability. A numerical example involving a planar quadrotor experiencing challenging ground effects demonstrates significant performance gains from the robust prediction method and online learning.
arXiv:2502.00550v2 Announce Type: replace Abstract: Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design...
arXiv:2606. 17233v1 Announce Type: new Abstract: In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.
The paper introduces a learning-based surrogate approach for stochastic optimization problems where uncertainty depends on the decision, modeled via a nonparametric regression. It constructs a surrogate that embeds iteratively updated Jacobian estimates, using an adaptive random design that focuses sampling near the current iterate to achieve dimension‑independent convergence of the Jacobian estimates. The resulting learning‑based stochastic prox‑linear (L‑SPL) algorithm demonstrates nonasymptotic convergence rates and outperforms existing methods in sample efficiency and objective value in numerical experiments.
arXiv:2603. 20467v2 Announce Type: replace-cross Abstract: Stochastic differential equations (SDEs), which serve as the governing equations for dynamical systems in a broad range of applications, can become cost-prohibitive for numerical simulation at scales necessary for quantifying key properties.
arXiv:2511.22648v2 Announce Type: replace Abstract: A spatially aware dictionary-free eigenfunction discovery (SADFED) framework is proposed for identification of low-rank Koopman models from data wi...
arXiv:2608. 03360v1 Announce Type: cross Abstract: Non-intrusive reduced-order models (NIROMs) have become a standard tool for approximating parametric partial differential equations from computer design of experiments while significantly reducing computational costs.
arXiv:2609.08740v1 Announce Type: new Abstract: In this paper we derive a Probably Approximately Correct (PAC)-Bayesian error bound for partially observed linear time-invariant (LTI) stochastic dynam...