2 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…