@article{DellnitzHuellermeierLueckeetal.2023, author = {Dellnitz, Michael and H{\"u}llermeier, Eyke and L{\"u}cke, Marvin and Ober-Bl{\"o}baum, Sina and Offen, Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, title = {Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning}, volume = {45}, journal = {SIAM Journal on Scientific Computing}, number = {2}, doi = {10.1137/21M1412682}, pages = {A579 -- A595}, year = {2023}, language = {en} } @article{LueckeHeitzigKoltaietal.2023, author = {L{\"u}cke, Marvin and Heitzig, Jobst and Koltai, P{\´e}ter and Molkethin, Nora and Winkelmann, Stefanie}, title = {Large population limits of Markov processes on random networks}, volume = {166}, journal = {Stochastic Processes and their Applications}, arxiv = {http://arxiv.org/abs/2210.02934}, doi = {10.1016/j.spa.2023.09.007}, year = {2023}, abstract = {We consider time-continuous Markovian discrete-state dynamics on random networks of interacting agents and study the large population limit. The dynamics are projected onto low-dimensional collective variables given by the shares of each discrete state in the system, or in certain subsystems, and general conditions for the convergence of the collective variable dynamics to a mean-field ordinary differential equation are proved. We discuss the convergence to this mean-field limit for a continuous-time noisy version of the so-called "voter model" on Erdős-R{\´e}nyi random graphs, on the stochastic block model, as well as on random regular graphs. Moreover, a heterogeneous population of agents is studied. For each of these types of interaction networks, we specify the convergence conditions in dependency on the corresponding model parameters.}, language = {en} }