Abstract
We discuss variable-sample strategies and consensus- and kinetic-based particle optimization methods for problems where the cost function represents the expected value of a random mapping. Variable-sample strategies replace the expected value by an approximation at each iteration of the optimization algorithm. We introduce a novel variable-sample inspired time-discrete consensus-type algorithm and demonstrate its computational efficiency. Subsequently, we present an alternative time-continuous kinetic-based description of the algorithm, which allows us to exploit tools of kinetic theory to conduct a comprehensive theoretical analysis. Finally, we test the consistency of the proposed modelling approaches through several numerical experiments.
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Abstract
We propose a method for the uncertainty quantification of nonlinear hyperbolic equations with many uncertain parameters. The method combines the stochastic finite volume method and tensor trains in a novel way: the physical space and time dimensions are kept as full tensors, while all stochastic dimensions are compressed together into a tensor train. The resulting hybrid format has one tensor train for each spatial cell and each time step. We adapt a MUSCL scheme to this hybrid format and show the feasibility of the approach using several classical test cases. A convergence study is done on the Burgers' equation with three stochastic parameters. We also solve the Burgers' equation for an increasing number of stochastic dimensions and show an example with the Euler equations. The presented method opens new avenues for combining uncertainty quantification with well-known numerical schemes for conservation law.
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Abstract
The discretization of reduced one-dimensional hyperbolic models of blood flow using the Lax-Friedrichs method is discussed. Employing the well-established central scheme in this domain significantly simplifies the implementation of specific boundary and coupling conditions in vascular networks accounting e.g. for a periodic heart beat, vascular occlusions, stented vessel segments and bifurcations. In particular, the coupling of system extensions modeling patient specific geometries and therapies can be realized without information on the eigenstructure of the models. For the derivation of the scheme and the coupling conditions a relaxation of the model is considered and its discrete relaxation limit evaluated. Moreover, a second order MUSCL-type extensions of the scheme is introduced. Numerical experiments in uncoupled and coupled cases that verify the consistency and convergence of the approach are presented.
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Abstract
Stabilization of partial differential equations is a topic of utmost importance in mathematics as well as in engineering sciences. Concerning one dimensional problems there exists a well developed theory. Due to numerous important applications the interest in boundary feedback control of multi-dimensional hyperbolic systems is increasing. In the present work we want to discuss the relation between some of the most recent results available in the literature. The key result of the present work is to show that the type of system discussed in Yang and Yong (2024) identifies a particular class which falls into the framework presented in Herty and Thein (2024).