2 citations · 2 across the 8 of their papers we have counts for
9 papers
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection
Jan Stenner, Hans Harder, Sebastian Peitz
This paper studies sparse sensor placement for control of Rayleigh-Bénard convection with multi-agent reinforcement learning. We train dense expert policies with windowed observati…
Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection
Tim Plotzki, Sebastian Peitz
Training reinforcement learning (RL) agents to control fluid dynamics systems is computationally expensive due to the high cost of direct numerical simulations (DNS) of the governi…
Automatic feature identification in least-squares policy iteration using the Koopman operator framework
Christian Mugisho Zagabe, Sebastian Peitz
In this paper, we present a Koopman autoencoder-based least-squares policy iteration (KAE-LSPI) algorithm in reinforcement learning (RL). The KAE-LSPI algorithm is based on reformu…
Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems
Hans Harder, Abhijeet Vishwasrao, Luca Guastoni +2
This paper is concerned with probabilistic techniques for forecasting dynamical systems described by partial differential equations (such as, for example, the Navier-Stokes equatio…
Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders
Fynn Fromme, Hans Harder, Christine Allen-Blanchette +1
The use of machine learning for modeling, understanding, and controlling large-scale physics systems is quickly gaining in popularity, with examples ranging from electromagnetism o…
Group-Convolutional Extended Dynamic Mode Decomposition
Hans Harder, Feliks Nüske, Friedrich M. Philipp +3
This paper explores the integration of symmetries into the Koopman-operator framework for the analysis and efficient learning of equivariant dynamical systems using a group-convolu…
Common pitfalls to avoid while using multiobjective optimization in machine learning
Junaid Akhter, Paul David Fährmann, Konstantin Sonntag +2
Recently, there has been an increasing interest in the application of multiobjective optimization (MOO) in machine learning (ML). This interest is driven by the numerous real-life…
Solving Partial Differential Equations with Equivariant Extreme Learning Machines
Hans Harder, Jean Rabault, Ricardo Vinuesa +2
We utilize extreme-learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individ…
On the continuity and smoothness of the value function in reinforcement learning and optimal control
Hans Harder, Sebastian Peitz
The value function plays a crucial role as a measure for the cumulative future reward an agent receives in both reinforcement learning and optimal control. It is therefore of inter…