15 citations · 26 across the 15 of their papers we have counts for
8 papers · 1 filter
Dynamic output-feedback stabilization of uncertain linear dynamics via digital twins
Philipp A. Guth, Karl Kunisch, Sergio S. Rodrigues +1
This work presents a digital twin framework for output-feedback stabilization and parameter identification in uncertain dynamical systems. A virtual model evolves in parallel with…
Multilevel Stochastic Gradient Descent for Risk-Averse PDE-Constrained Optimization
Niklas Baumgarten, Philipp A. Guth, David Schneiderhan +1
We present recent advances in applying and analyzing multilevel stochastic gradient descent algorithms to risk-averse, three-dimensional PDE-constrained optimization problems. The…
Approximation of risk-averse optimal feedback control
Philipp A. Guth, Karl Kunisch
The challenge of constructing feedback control laws for risk-averse optimal control of partial differential equations (PDEs) with random coefficients is addressed. The control obje…
Quasi-Monte Carlo integration for feedback control under uncertainty
Philipp A. Guth, Peter Kritzer, Karl Kunisch
A control in feedback form is derived for linear quadratic, time-invariant optimal control problems subject to parabolic partial differential equations with coefficients depending…
Tracking optimal feedback control under uncertain parameters
Philipp A. Guth, Karl Kunisch, Sergio S. Rodrigues
Optimal control problems of tracking type for a class of linear systems with uncertain parameters in the dynamics are investigated. An affine tracking feedback control input is obt…
Stabilization of uncertain linear dynamics: an offline-online strategy
Philipp A. Guth, Karl Kunisch, Sérgio S. Rodrigues
A strategy is proposed for adaptive stabilization of linear systems, depending on an uncertain parameter. Offline, the Riccati stabilizing feedback input control operators, corresp…
Ensemble Feedback Stabilization of Linear Systems
Philipp A. Guth, Karl Kunisch, Sergio S. Rodrigues
Stabilization of linear control systems with parameter-dependent system matrices is investigated. A Riccati based feedback mechanism is proposed and analyzed. It is constructed by…
One-shot Learning of Surrogates in PDE-constrained Optimization Under Uncertainty
Philipp A. Guth, Claudia Schillings, Simon Weissmann
We propose a general framework for machine learning based optimization under uncertainty. Our approach replaces the complex forward model by a surrogate, which is learned simultane…