Veranstaltungen

Oberseminar Wissenschaftliches Rechnen (Prof. Martin Gugat)

L1-control cost and the finite–time turnpike property

CSIP 2023 - Chemnitz Symposium on Inverse Problems | 8.-10.11.2023 | Würzburg

Oberseminar Wissenschaftliches Rechnen (Prof. Hao Chen)

Differential Riccati equations play a crucial role in optimal control and estimation theory.

Oberseminar Wissenschaftliches Rechnen (Prof. Vittorio Colao)

On some iterative algorithms arising in metric fixed point Theory

Oberseminar Wissenschaftliches Rechnen (Prof. Vittorio Colao)

On some iterative algorithms arising in metric fixed point Theory

Oberseminar Wissenschaftliches Rechnen (Dr. Housen Li)

Adaptive minimax optimality in statistical inverse problems

Oberseminar Wissenschaftliches Rechnen (Dr. Tim Breitenbach)

Optimization and Machine learning in action for data center automation, single cell analysis and glioblastoma research

Oberseminar Wissenschaftliches Rechnen (Dr. Remo Kretschmann)

Optimal and Bayesian regularized hypothesis testing in statistical inverse problems

Oberseminar Wissenschaftliches Rechnen (Dr. Stefana Anita)

Nonlocal Optimal Control for a Nonlinear Fokker-Planck Equation

Oberseminar Wissenschaftliches Rechnen (Dr. Vesa Kaarnioja)

Modeling domain uncertainty using periodic random variables with application to elliptic PDEs

Oberseminar Wissenschaftliches Rechnen (Dr. Florian Wechsung, Courant Institute of Mathematical Sciences New York University)

Direct Stellarator coil optimisation under uncertainty

Oberseminar Wissenschaftliches Rechnen (Prof. Georg Stadler, Courant Institute of Mathematical Sciences New York University)

Optimal control of PDEs under uncertainty with joint chance state constraints

Oberseminar Wissenschaftliches Rechnen (Jun.-Prof. Anne Wald, Georg-August-Universität Göttingen)

Time-dependence as modelling inexactness in inverse problems: regularization and application in dynamic imaging

Oberseminar Wissenschaftliches Rechnen (Prof. Dr. Bruno Buonomo, University of Naples Federico II)

Modeling vaccine hesitancy using a behavioral epidemiology approach

Oberseminar Wissenschaftliches Rechnen (Markus Pohlmann, Georg-August-Universität Göttingen)

Minimax detection of frame-based signals in statistical inverse problems