Physics-informed neural networks (PINNs) have recently become a popular method for
solving forward and inverse problems governed by partial differential equations (PDEs). By …

PDE-constrained optimization refers to the optimization of systems governed by PDEs. The
simulation problem is to solve the PDEs for the state variables (eg, displacement, velocity …

The paper deals with a numerical method for aerodynamic shape optimization. It is based on
simultaneous pseudo-timestepping in which stationary states are obtained by solving the …

Recent research has used deep learning to develop partial differential equation (PDE)
models in science and engineering. The functional form of the PDE is determined by a …

Large scale non-parametric applied shape optimization for computational fluid dynamics is
considered. Treating a shape optimization problem as a standard optimal control problem by …

We consider the discretized optimality system of a special class of elliptic optimal control
problems and propose an all-at-once multigrid method for solving this discretized system …

Improving efficiency and yield of a parabolic trough collector (PTC) field is of paramount
technological importance. Here, an attempt toward this goal is made through combined …

The single-step one-shot method has proven to be very efficient for PDE-constrained
optimization where the partial differential equation (PDE) is solved by an iterative fixed point …

Plasma edge transport codes play a key role in the design of future divertor concepts. Their
long simulation times in combination with a large number of control parameters turn the …

One-shot optimization aims at attaining feasibility and optimality simultaneously, especially
on problems where even the linearized constraint equations cannot be resolved …