1 citations · 2 across the 15 of their papers we have counts for
5 papers · 1 filter
Rethinking Physics-Informed Regression Beyond Training Loops and Bespoke Architectures
Lorenzo Sabug, Eric Kerrigan
We revisit the problem of physics-informed regression, and propose a method that directly computes the state at the prediction point, simultaneously with the derivative and curvatu…
Optimistic vs Pessimistic Uncertainty Model Unfalsification
Jannes Hühnerbein, Jad Wehbeh, Eric C. Kerrigan
We present a novel, input-output data-driven approach to uncertainty model identification. As the true bounds and distributions of system uncertainties ultimately remain unknown, w…
Smooth Logic Constraints in Nonlinear Optimization and Optimal Control Problems
J. Wehbeh, E. C. Kerrigan
In some optimal control problems, complex relationships between states and inputs cannot be easily represented using continuous constraints, necessitating the use of discrete logic…
Update-Aware Robust Optimal Model Predictive Control for Nonlinear Systems
J. Wehbeh, E. C. Kerrigan
Robust optimal or min-max model predictive control (MPC) approaches aim to guarantee constraint satisfaction over a known, bounded uncertainty set while minimizing a worst-case per…
State-Dependent Uncertainty Modeling in Robust Optimal Control Problems through Generalized Semi-Infinite Programming
J. Wehbeh, E. C. Kerrigan
Generalized semi-infinite programs (generalized SIPs) are problems featuring a finite number of decision variables but an infinite number of constraints. They differ from standard…