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Journal Article

Reconstructing in-depth activity for chaotic 3D spatiotemporal excitable media models based on surface data

MPS-Authors
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Stenger,  R.
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons240972

Herzog,  S.
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons256731

Kottlarz,  I.
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons247490

Rüchardt,  B.
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons173583

Luther,  S.
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons173613

Parlitz,  U.
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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5.0126824.pdf
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Citation

Stenger, R., Herzog, S., Kottlarz, I., Rüchardt, B., Luther, S., Wörgötter, F., et al. (2023). Reconstructing in-depth activity for chaotic 3D spatiotemporal excitable media models based on surface data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33: 013134. doi:10.1063/5.0126824.


Cite as: https://hdl.handle.net/21.11116/0000-000C-808B-6
Abstract
Motivated by potential applications in cardiac research, we consider the task of reconstructing the dynamics within a spatiotemporal chaotic 3D excitable medium from partial observations at the surface. Three artificial neural network methods (a spatiotemporal convolutional long-short-term-memory, an autoencoder, and a diffusion model based on the U-Net architecture) are trained to predict the dynamics in deeper layers of a cube from observational data at the surface using data generated by the Barkley model on a 3D domain. The results show that despite the high-dimensional chaotic dynamics of this system, such cross-prediction is possible, but non-trivial and as expected, its quality decreases with increasing prediction depth.