3 papers
math.NA2026
Stochastic nonlocal traffic flow models with Markovian noise
Timo Böhme, Simone Göttlich, Andreas Neuenkirch
We extend our recently introduced stochastic nonlocal traffic flow model to more general random perturbations, including Markovian noise derived from a discretized Jacobi-type stoc…
stat.ML2024
Using Low-Discrepancy Points for Data Compression in Machine Learning: An Experimental Comparison
Simone Göttlich, Jacob Heieck, Andreas Neuenkirch
Low-discrepancy points (also called Quasi-Monte Carlo points) are deterministically and cleverly chosen point sets in the unit cube, which provide an approximation of the uniform d…
math.NA2024
A nonlocal traffic flow model with stochastic velocity
Timo Böhme, Simone Göttlich, Andreas Neuenkirch
In this paper, we investigate a nonlocal traffic flow model based on a scalar conservation law, where a stochastic velocity function is assumed. In addition to the modeling, theore…