274 citations · 1k across the 43 of their papers we have counts for
6 papers · 1 filter
Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning
Jan Stenner, Alexander Kilian, Sebastian Peitz +1
This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers.…
Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces
Septimus Boshoff, Sebastian Peitz, Stefan Klus
The Koopman operator, as a linear representation of a nonlinear dynamical system, has been attracting attention in many fields of science. Recently, Koopman operator theory has bee…
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…
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…
Deep Model Predictive Control with Online Learning for Complex Physical Systems
Katharina Bieker, Sebastian Peitz, Steven L. Brunton +2
The control of complex systems is of critical importance in many branches of science, engineering, and industry. Controlling an unsteady fluid flow is particularly important, as fl…