Publications

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International Conference Papers


ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning

Published in The Thirteenth International Conference on Learning Representations (ICLR 2025), 2025

BibTeX:

@inproceedings{schwencke2025anagram,
  title={{ANaGRAM}: {A} Natural Gradient Relative to Adapted Model for efficient {PINNs} learning},
  author={Schwencke, Nilo and Furtlehner, Cyril},
  booktitle={The Thirteenth International Conference on Learning Representations ({ICLR})},
  year={2025},
  url={https://proceedings.iclr.cc/paper_files/paper/2025/hash/38b445e98823a02479582302ee3aa8e4-Abstract-Conference.html}
}

Recommended citation: Nilo Schwencke, Cyril Furtlehner, "ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning." ICLR, 2025.
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Preprints


AMStraMGRAM: Adaptive Multi-cutoff Strategy Modification for ANaGRAM

Published in arXiv, 2025

BibTeX:

@misc{schwencke2025amstramgram,
  title={{AMStraMGRAM}: Adaptive Multi-cutoff Strategy Modification for {ANaGRAM}},
  author={Schwencke, Nilo and Rousselot, Cyriaque and Shilova, Alena and Furtlehner, Cyril},
  year={2025},
  eprint={2510.15998},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Recommended citation: Nilo Schwencke, Cyriaque Rousselot, Alena Shilova, Cyril Furtlehner, "AMStraMGRAM: Adaptive Multi-cutoff Strategy Modification for ANaGRAM." arXiv, 2025.
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Workshop Papers


Implicit function theorem in Physics-Informed Neural Networks to solve parameterized differential equations

Published in EurIPS 2025 Workshop DiffSys, 2025

BibTeX:

@inproceedings{marieanne2025ift,
  title={Implicit function theorem in {Physics-Informed Neural Networks} to solve parameterized differential equations},
  author={Marie-Anne, Julien and Rousselot, Cyriaque and Schwencke, Nilo and Shilova, Alena},
  booktitle={{EurIPS} 2025 Workshop on Differentiable Systems ({DiffSys})},
  year={2025},
  url={https://openreview.net/forum?id=KjyGLUleWh}
}

Recommended citation: Julien Marie-Anne, Cyriaque Rousselot, Nilo Schwencke, Alena Shilova, "Implicit function theorem in Physics-Informed Neural Networks to solve parameterized differential equations." EurIPS 2025 Workshop DiffSys, 2025.
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Theses


Natural gradients and kernel methods for Physics Informed Neural Networks (PINNs)

Published in Université Paris-Saclay, 2025

This dissertation addresses limitations in Physics-Informed Neural Networks (PINNs) through two complementary approaches. Algorithmically, it develops improved training schemes combining kernel methods and natural gradients. Theoretically, it grounds PINNs in rigorous mathematics using Reproducing Kernel Hilbert Spaces (RKHS) and spectral analysis.

Recommended citation: Nilo Schwencke, "Natural gradients and kernel methods for Physics Informed Neural Networks (PINNs)." PhD thesis, Université Paris-Saclay, 2025.
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Simulation of lithium ions batteries using physics informed neural networks

Published in Karlsruhe Institute of Technology (KIT), 2023

Master thesis applying Physics-Informed Neural Networks (PINNs) to the simulation of lithium-ion batteries, with a focus on the underlying PDEs governing electrochemical dynamics.

Recommended citation: Nilo Schwencke, "Simulation of lithium ions batteries using physics informed neural networks." Master thesis, Karlsruhe Institute of Technology (KIT), 2023.