Scenario-based model predictive control (MPC) methods introduce recourse into optimal
control and can thus reduce the conservativeness inherent to open-loop robust MPC …

We present a model reduction formulation for parametrized nonlinear partial differential
equations (PDEs). Our approach builds on two ingredients: reduced basis (RB) spaces …

A fast and simple grid generation can be achieved by non-standard discretization methods
where the mesh does not conform to the boundary or the internal interfaces of the problem …

We present a model reduction formulation for parametrized nonlinear partial differential
equations (PDEs) associated with steady hyperbolic and convection-dominated …

We present a systematic computational framework for generating positive quadrature rules
in multiple dimensions on general geometries. A direct moment-matching formulation that …

2. Hyper-reduction methods 2.1 Parametrized integrals 2.2 Empirical quadrature methods
2.3 Empirical interpolation methods 2.4 Integral interpolation methods 3. First-order …

The majority of work on chance constrained model predictive control (MPC) for stochastic
systems adopts the concept of implicit constraint backoffs for handling state chance …

Stochastic spectral methods have achieved a great success in the uncertainty quantification
of many engineering problems, including variation-aware electronic and photonic design …

Learning control has become an appealing alternative to the derivation of control laws
based on classic control theory. However, a major shortcoming of learning control is the lack …

We consider fully symmetric quadrature rules with positive weights, and with nodes lying
inside the 3,…, 6 dimensional simplex (so-called PI-type). PI-type fully symmetric quadrature …