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Two major problems in modern cities are air contamination and road congestion. They are closely related and present a similar origin: traffic flow. To face these problems, local governments impose traffic restrictions to prevent the entry of vehicles into sensitive areas, with the final aim of dropping down air pollution levels. However, these restrictions force drivers to look for alternative routes that usually generate congestions, implying both longer travel times and higher levels of air pollution. In this work, combining optimal control of partial differential equations and computational modelling, we formulate a multi-objective control problem with air pollution and drivers travel time as objectives and look for its optimal solutions in the sense of Stackelberg. In this problem, local government (the leader) implements traffic restrictions meanwhile the set of drivers (the follower) acts choosing travel preferences against leader constraints. Numerically, the discretized problem is solved by combining genetic-elitist algorithms and interior-point methods, and computational results for a realistic case posed in the Guadalajara Metropolitan Area (Mexico) are shown.
Least squares Monte Carlo methods are a popular numerical approximation method for solving stochastic control problems. Based on dynamic programming, their key feature is the approximation of the conditional expectation of future rewards by linear le
This work discusses the finite element discretization of an optimal control problem for the linear wave equation with time-dependent controls of bounded variation. The main focus lies on the convergence analysis of the discretization method. The stat
In PDE-constrained optimization, proper orthogonal decomposition (POD) provides a surrogate model of a (potentially expensive) PDE discretization, on which optimization iterations are executed. Because POD models usually provide good approximation qu
We present a data-driven point of view for rare events, which represent conformational transitions in biochemical reactions modeled by over-damped Langevin dynamics on manifolds in high dimensions. We first reinterpret the transition state theory and
This paper studies a stochastic robotic surveillance problem where a mobile robot moves randomly on a graph to capture a potential intruder that strategically attacks a location on the graph. The intruder is assumed to be omniscient: it knows the cur