274 citations · 1.8k across the 48 of their papers we have counts for
19 papers · 1 filter
Functional dynamic mode decomposition: Learning infinite-dimensional systems from data
Stefan Klus, Eirini Ioannou
Dynamic mode decomposition (DMD) is a data-driven method that computes the best linear approximation of the underlying dynamical system and decomposes the dynamics into a superposi…
Data-driven approximation of transfer operators for mean-field stochastic differential equations
Eirini Ioannou, Stefan Klus, Gonçalo dos Reis
Mean-field stochastic differential equations, also called McKean--Vlasov equations, are the limiting equations of interacting particle systems with fully symmetric interaction pote…
Koopman-Based Surrogate Models for Multi-Objective Optimization of Agent-Based Systems
Jan-Hendrik Niemann, Stefan Klus, Nataša Djurdjevac Conrad +1
Agent-based models (ABMs) provide an intuitive and powerful framework for studying social dynamics by modeling the interactions of individuals from the perspective of each individu…
Koopman-based spectral clustering of directed and time-evolving graphs
Stefan Klus, Natasa Djurdjevac Conrad
While spectral clustering algorithms for undirected graphs are well established and have been successfully applied to unsupervised machine learning problems ranging from image segm…
Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel +8
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastab…
Data-driven model reduction of agent-based systems using the Koopman generator
Jan-Hendrik Niemann, Stefan Klus, Christof Schütte
The dynamical behavior of social systems can be described by agent-based models. Although single agents follow easily explainable rules, complex time-evolving patterns emerge due t…