Optimal Approximation of the First-Order Corrector in Multiscale Stochastic Elliptic PDE. - Caroline Geiersbach; Clemens Heitzinger; Gerhard Tulzer (SIAM/ASA J. Uncertain. Quantification, 2016)

Projected Stochastic Gradients for Convex Constrained Problems in Hilbert Spaces. - Caroline Geiersbach; Georg Ch. Pflug (SIAM J. Optim., 2019)

A Stochastic Gradient Method With Mesh Refinement for PDE-Constrained Optimization Under Uncertainty. - Caroline Geiersbach; Winnifried Wollner (SIAM J. Sci. Comput., 2020)

Stochastic proximal gradient methods for nonconvex problems in Hilbert spaces. - Caroline Geiersbach; Teresa Scarinci (Comput. Optim. Appl., 2021)

PDE-constrained shape optimization: towards product shape spaces and stochastic models. - Caroline Geiersbach; Estefanía Loayza-Romero; Kathrin Welker (CoRR, 2021)

Stochastic Approximation for Optimization in Shape Spaces. - Caroline Geiersbach; Estefanía Loayza-Romero; Kathrin Welker (SIAM J. Optim., 2021)

Optimality Conditions for Convex Stochastic Optimization Problems in Banach Spaces with Almost Sure State Constraints. - Caroline Geiersbach; Winnifried Wollner (SIAM J. Optim., 2021)

Stochastic Augmented Lagrangian Method in Riemannian Shape Manifolds. - Caroline Geiersbach; Tim Suchan; Kathrin Welker (J. Optim. Theory Appl., 2024)

Optimality Conditions in Control Problems with Random State Constraints in Probabilistic or Almost Sure Form. - Caroline Geiersbach; René Henrion (Math. Oper. Res., 2025)

Numerical Solution of an Optimal Control Problem with Probabilistic and Almost Sure State Constraints. - Caroline Geiersbach; René Henrion; Pedro Pérez-Aros (J. Optim. Theory Appl., 2025)