3 papers
eess.SY2026
On Improved Statistical Accuracy of Low-Order Polynomial Chaos Approximations
Vedang M. Deshpande, Raktim Bhattacharya
Polynomial chaos expansions provide surrogate models for stochastic systems, with coefficients typically derived using Galerkin projection, stochastic collocation, or least squares…
cs.RO2025
Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing
Iman Askari, Yebin Wang, Vedeng M. Deshpande +1
Safe and efficient motion planning is of fundamental importance for autonomous vehicles. This paper investigates motion planning based on nonlinear model predictive control (NMPC)…
cs.LG2025
Meta-Learning for Physically-Constrained Neural System Identification
Ankush Chakrabarty, Gordon Wichern, Vedang M. Deshpande +3
We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorpor…