@article{40171,
  abstract     = {{We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problem can be transformed into a multi-agent control problem with many identical, uncoupled agents. Furthermore, using the fact that information is transported with finite velocity in many cases, the dimension of the agents’ environment can be drastically reduced using a convolution operation over the state space of the PDE, by which we effectively tackle the curse of dimensionality otherwise present in deep reinforcement learning. In this setting, the complexity can be flexibly adjusted via the kernel width or by using a stride greater than one (meaning that we do not place an actuator at each sensor location). Moreover, scaling from smaller to larger domains – or the transfer between different domains – becomes a straightforward task requiring little effort. We demonstrate the performance of the proposed framework using several PDE examples with increasing complexity, where stabilization is achieved by training a low-dimensional deep deterministic policy gradient agent using minimal computing resources.}},
  author       = {{Peitz, Sebastian and Stenner, Jan and Chidananda, Vikas and Wallscheid, Oliver and Brunton, Steven L. and Taira, Kunihiko}},
  journal      = {{Physica D: Nonlinear Phenomena}},
  pages        = {{134096}},
  publisher    = {{Elsevier}},
  title        = {{{Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning}}},
  doi          = {{10.1016/j.physd.2024.134096}},
  volume       = {{461}},
  year         = {{2024}},
}

@inproceedings{54838,
  author       = {{Boshoff, Septimus and Stenner, Jan and Weber, Daniel and Meyer, Marvin and Chidananda, Vikas and Peitz, Sebastian and Wallscheid, Oliver}},
  booktitle    = {{IEEE Power and Energy Student Summit (PESS)}},
  isbn         = {{978-3-8007-6318-4}},
  pages        = {{124--129}},
  publisher    = {{VDE}},
  title        = {{{Hybrid control of interconnected power converters using both expert-driven droop and data-driven reinforcement learning approaches}}},
  year         = {{2023}},
}

@inproceedings{54839,
  author       = {{Meyer, Marvin and Weber, Daniel and Chidananda, Vikas and Schweins, Oliver and Stenner, Jan and Boshoff, Septimus and Peitz, Sebastian and Wallscheid, Oliver}},
  booktitle    = {{IEEE Power and Energy Student Summit (PESS)}},
  isbn         = {{978-3-8007-6318-4}},
  pages        = {{112--117}},
  publisher    = {{VDE}},
  title        = {{{ElectricGrid.jl – Automated modeling of decentralized electrical energy grids}}},
  year         = {{2023}},
}

@article{46784,
  author       = {{Wallscheid, Oliver and Peitz, Sebastian and Stenner, Jan and Weber, Daniel and Boshoff, Septimus and Meyer, Marvin and Chidananda, Vikas and Schweins, Oliver}},
  issn         = {{2475-9066}},
  journal      = {{Journal of Open Source Software}},
  keywords     = {{General Earth and Planetary Sciences, General Environmental Science}},
  number       = {{89}},
  publisher    = {{The Open Journal}},
  title        = {{{ElectricGrid.jl - A Julia-based modeling and simulationtool for power electronics-driven electric energy grids}}},
  doi          = {{10.21105/joss.05616}},
  volume       = {{8}},
  year         = {{2023}},
}