@masterthesis{Rabben2019, type = {Bachelor Thesis}, author = {Rabben, Robert Julian}, title = {Bestimmung invarianter Unterr{\"a}ume des Koopman-Operators einer Overdamped Langevin-Dynamik mithilfe eines Muli-Layer Neural Networks}, year = {2019}, language = {de} } @article{RabbenRayWeber2020, author = {Rabben, Robert Julian and Ray, Sourav and Weber, Marcus}, title = {ISOKANN: Invariant subspaces of Koopman operators learned by a neural network}, volume = {153}, journal = {The Journal of Chemical Physics}, number = {11}, doi = {10.1063/5.0015132}, pages = {114109}, year = {2020}, abstract = {The problem of determining the rate of rare events in dynamical systems is quite well-known but still difficult to solve. Recent attempts to overcome this problem exploit the fact that dynamic systems can be represented by a linear operator, such as the Koopman operator. Mathematically, the rare event problem comes down to the difficulty in finding invariant subspaces of these Koopman operators K. In this article, we describe a method to learn basis functions of invariant subspaces using an artificial neural Network.}, language = {en} } @misc{Rabben2022, type = {Master Thesis}, author = {Rabben, Robert Julian}, title = {Ein holistischer Ansatz zur Analyse molekularer Konformationen auf der Basis von ISOKANN}, year = {2022}, language = {de} } @inproceedings{SikorskiRabbenChewleetal.2025, author = {Sikorski, Alexander and Rabben, Robert Julian and Chewle, Surahit and Weber, Marcus}, title = {Capturing the Macroscopic Behaviour of Molecular Dynamics with Membership Functions}, booktitle = {Mathematical Optimization for Machine Learning: Proceedings of the MATH+ Thematic Einstein Semester 2023}, editor = {Fackeldey, K.}, publisher = {De Gruyter}, arxiv = {http://arxiv.org/abs/2404.10523}, doi = {10.1515/9783111376776-004}, pages = {41 -- 58}, year = {2025}, abstract = {Markov processes serve as foundational models in many scientific disciplines, such as molecular dynamics, and their simulation forms a common basis for analysis. While simulations produce useful trajectories, obtaining macroscopic information directly from microstate data presents significant challenges. This paper addresses this gap by introducing the concept of membership functions being the macrostates themselves. We derive equations for the holding times of these macrostates and demonstrate their consistency with the classical definition. Furthermore, we discuss the application of the ISOKANN method for learning these quantities from simulation data. In addition, we present a novel method for extracting transition paths based on the ISOKANN results and demonstrate its efficacy by applying it to simulations of the 𝜇-opioid receptor. With this approach we provide a new perspective on analyzing the macroscopic behaviour of Markov systems.}, language = {en} }