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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2410.18356 |
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| _version_ | 1866929556716257280 |
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| author | Youel, Harry Prestwood, Daniel Lee, Oscar Wei, Tianyi Stenning, Kilian D. Gartside, Jack C. Branford, Will R. Everschor-Sitte, Karin Kurebayashi, Hidekazu |
| author_facet | Youel, Harry Prestwood, Daniel Lee, Oscar Wei, Tianyi Stenning, Kilian D. Gartside, Jack C. Branford, Will R. Everschor-Sitte, Karin Kurebayashi, Hidekazu |
| contents | Physical reservoir computing (PRC) is a computing framework that harnesses the intrinsic dynamics of physical systems for computation. It offers a promising energy-efficient alternative to traditional von Neumann computing for certain tasks, particularly those demanding both memory and nonlinearity. As PRC is implemented across a broad variety of physical systems, the need increases for standardised tools for data processing and model training. In this manuscript, we introduce PRCpy, an open-source Python library designed to simplify the implementation and assessment of PRC for researchers. The package provides a high-level interface for data handling, preprocessing, model training, and evaluation. Key concepts are described and accompanied by experimental data on two benchmark problems: nonlinear transformation and future forecasting of chaotic signals. Throughout this manuscript, which will be updated as a rolling release, we aim to facilitate researchers from diverse disciplines to prioritise evaluating the computational benefits of the physical properties of their systems by simplifying data processing, model training and evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18356 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PRCpy: A Python Package for Processing of Physical Reservoir Computing Youel, Harry Prestwood, Daniel Lee, Oscar Wei, Tianyi Stenning, Kilian D. Gartside, Jack C. Branford, Will R. Everschor-Sitte, Karin Kurebayashi, Hidekazu Computational Engineering, Finance, and Science Computational Physics Physical reservoir computing (PRC) is a computing framework that harnesses the intrinsic dynamics of physical systems for computation. It offers a promising energy-efficient alternative to traditional von Neumann computing for certain tasks, particularly those demanding both memory and nonlinearity. As PRC is implemented across a broad variety of physical systems, the need increases for standardised tools for data processing and model training. In this manuscript, we introduce PRCpy, an open-source Python library designed to simplify the implementation and assessment of PRC for researchers. The package provides a high-level interface for data handling, preprocessing, model training, and evaluation. Key concepts are described and accompanied by experimental data on two benchmark problems: nonlinear transformation and future forecasting of chaotic signals. Throughout this manuscript, which will be updated as a rolling release, we aim to facilitate researchers from diverse disciplines to prioritise evaluating the computational benefits of the physical properties of their systems by simplifying data processing, model training and evaluation. |
| title | PRCpy: A Python Package for Processing of Physical Reservoir Computing |
| topic | Computational Engineering, Finance, and Science Computational Physics |
| url | https://arxiv.org/abs/2410.18356 |