A quasi-Monte Carlo Method for an Optimal Control Problem Under Uncertainty. - Philipp A. Guth; Vesa Kaarnioja; Frances Y. Kuo; Claudia Schillings; Ian H. Sloan (CoRR, 2019)

Ensemble Kalman filter for neural network based one-shot inversion. - Philipp A. Guth; Claudia Schillings; Simon Weissmann (CoRR, 2020)

A General Framework for Machine Learning based Optimization Under Uncertainty. - Philipp A. Guth; Claudia Schillings; Simon Weissmann (CoRR, 2021)

A Quasi-Monte Carlo Method for Optimal Control Under Uncertainty. - Philipp A. Guth; Vesa Kaarnioja; Frances Y. Kuo; Claudia Schillings; Ian H. Sloan (SIAM/ASA J. Uncertain. Quantification, 2021)

Application of dimension truncation error analysis to high-dimensional function approximation. - Philipp A. Guth; Vesa Kaarnioja (CoRR, 2023)

Multilevel quasi-Monte Carlo for optimization under uncertainty. - Philipp A. Guth; Andreas Van Barel (Numerische Mathematik, 2023)

Quasi-Monte Carlo integration for feedback control under uncertainty. - Philipp A. Guth; Peter Kritzer; Karl Kunisch (CoRR, 2024)

Quasi-Monte Carlo for partial differential equations with generalized Gaussian input uncertainty. - Philipp A. Guth; Vesa Kaarnioja (CoRR, 2024)

One-Shot Learning of Surrogates in PDE-Constrained Optimization under Uncertainty. - Philipp A. Guth; Claudia Schillings; Simon Weissmann (SIAM/ASA J. Uncertain. Quantification, 2024)

Generalized Dimension Truncation Error Analysis for High-Dimensional Numerical Integration: Lognormal Setting and Beyond. - Philipp A. Guth; Vesa Kaarnioja (SIAM J. Numer. Anal., 2024)

Parabolic PDE-constrained optimal control under uncertainty with entropic risk measure using quasi-Monte Carlo integration. - Philipp A. Guth; Vesa Kaarnioja; Frances Y. Kuo; Claudia Schillings; Ian H. Sloan (Numerische Mathematik, 2024)