We consider numerical approaches for deterministic, finite-dimensional optimal control
problems whose dynamics depend on unknown or uncertain parameters. We seek to …

A learning technique for finite horizon optimal control problems and its approximation based
on polynomials is analyzed. It allows to circumvent, in part, the curse dimensionality which is …

Two related numerical schemes for the realization of the Mortensen observer or minimum
energy estimator for the state reconstruction of nonlinear dynamical systems subject to …

Approximation of high dimensional functions is in the focus of machine learning and data-
based scientific computing. In many applications, empirical risk minimisation techniques …

In this work the approximation of non necessarily smooth value functions associated to
infinite horizon optimal control problems via sequences of consistent feedback laws based …

We propose nonuniform data-driven parameter distributions for neural network initialization
based on derivative data of the function to be approximated. These parameter distributions …

In these notes we present some first ideas on the composition of tensor trains for the use in
scientific computing. We discuss the relation to deep neural networks and the potential role …

In this work the synthesis of approximate optimal and smooth feedback laws for infinite
horizon optimal control problems is addressed. In this regards, $ L^{p} $ type error bounds of …