274 citations · 1k across the 43 of their papers we have counts for
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Error analysis of kernel EDMD for prediction and control in the Koopman framework
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the Koopman operator for deterministic and stochastic (control) systems. This operator is…
Multiobjective Optimization of Non-Smooth PDE-Constrained Problems
Marco Bernreuther, Michael Dellnitz, Bennet Gebken +4
Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimizati…
A multiobjective continuation method to compute the regularization path of deep neural networks
Augustina C. Amakor, Konstantin Sonntag, Sebastian Peitz
Sparsity is a highly desired feature in deep neural networks (DNNs) since it ensures numerical efficiency, improves the interpretability of models (due to the smaller number of rel…
Equivariance and partial observations in Koopman operator theory for partial differential equations
Sebastian Peitz, Hans Harder, Feliks Nüske +3
The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems. The main reason is the enormous potential of identifying line…
Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping
Konstantin Sonntag, Sebastian Peitz
We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping.…
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs
Stefan Werner, Sebastian Peitz
The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by part…
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning
Sebastian Peitz, Jan Stenner, Vikas Chidananda +3
We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical syste…
Error bounds for kernel-based approximations of the Koopman operator
Friedrich Philipp, Manuel Schaller, Karl Worthmann +2
We consider the data-driven approximation of the Koopman operator for stochastic differential equations on reproducing kernel Hilbert spaces (RKHS). Our focus is on the estimation…