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From the 2 of 5 linked papers with an AI index.

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5 papers

math.OC2026

Dynamic output-feedback stabilization of uncertain linear dynamics via digital twins

Philipp A. Guth, Karl Kunisch, Sergio S. Rodrigues +1

The paper proposes a digital‑twin framework that runs alongside an uncertain linear system, using real‑time data to estimate the system state and parameters while generating a stab…

math.NA2026

On the optimality of dimension truncation error rates for a class of parametric partial differential equations

Philipp A. Guth, Vesa Kaarnioja

The paper analyzes the error introduced when infinite-dimensional random field inputs in parametric PDEs are truncated to finite dimensions, and proves that the known dimension‑tru…

math.OC2026

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…

math.NA2025

Quasi-Monte Carlo for partial differential equations with generalized Gaussian input uncertainty

Philipp A. Guth, Vesa Kaarnioja

There has been a surge of interest in uncertainty quantification for parametric partial differential equations (PDEs) with Gevrey regular inputs. The Gevrey class contains function…

math.OC2025

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…