Title: Probabilistic Contsrained Optimization with ODEs and PDEs 

Abstract:

Uncertainty often plays an important role in gas transport and probabilistic constraints are an excellent modeling tool to obtain controls and other quantities that are robust against perturbations. To efficiently evaluate the probabilistic constraint, we present an approach based on kernel density estimation, such that the probabilistic constrained optimization problem can be considered as classical nonllinear problem, allowing us to apply classical nonlinear optimization theory.
As an application, we consider and analyze the steady state and the transient gas flow in pipeline networks. We introduce the modelling based on the isothermal Euler equations including random boundary data, leading to optimal control problems with probabilistic constraints.

Schuster_Seoul2025