Abstract
The paper aims to present a description of the tm-flow library for the “flowly” construction and training of polynomial neural networks (PNN) for time-evolving process prediction. The introduced polynomial models have a strong relationship to dynamic systems that can be described by a system of nonlinear differential equations. The paper provides implementation details and an explanation of training strategies, along with a few illustrative numerical examples. The source code is available at https://github.com/PNN-Lab/tmflow/.
Supported by Saint Petersburg State University, project ID: 94029367.
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Klimenko, I., Golovkina, A., Ruzhnikov, V. (2023). Polynomial Neural Layers for Numerical Modeling of Dynamical Processes. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14109. Springer, Cham. https://doi.org/10.1007/978-3-031-37120-2_17
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DOI: https://doi.org/10.1007/978-3-031-37120-2_17
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