Emerging opportunities and challenges for the future of reservoir computing
Abstract Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving hi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-45187-1 |
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| author | Min Yan Can Huang Peter Bienstman Peter Tino Wei Lin Jie Sun |
| author_facet | Min Yan Can Huang Peter Bienstman Peter Tino Wei Lin Jie Sun |
| author_sort | Min Yan |
| collection | DOAJ |
| description | Abstract Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines. |
| format | Article |
| id | doaj-art-187593152b414e119b48e2e38ddb08b1 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-187593152b414e119b48e2e38ddb08b12024-11-24T12:32:12ZengNature PortfolioNature Communications2041-17232024-03-0115111810.1038/s41467-024-45187-1Emerging opportunities and challenges for the future of reservoir computingMin Yan0Can Huang1Peter Bienstman2Peter Tino3Wei Lin4Jie Sun5Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd.Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd.Photonics Research Group, Department of Information Technology, Ghent UniversitySchool of Computer Science, The University of BirminghamResearch Institute of Intelligent Complex Systems, Fudan UniversityTheory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd.Abstract Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines.https://doi.org/10.1038/s41467-024-45187-1 |
| spellingShingle | Min Yan Can Huang Peter Bienstman Peter Tino Wei Lin Jie Sun Emerging opportunities and challenges for the future of reservoir computing Nature Communications |
| title | Emerging opportunities and challenges for the future of reservoir computing |
| title_full | Emerging opportunities and challenges for the future of reservoir computing |
| title_fullStr | Emerging opportunities and challenges for the future of reservoir computing |
| title_full_unstemmed | Emerging opportunities and challenges for the future of reservoir computing |
| title_short | Emerging opportunities and challenges for the future of reservoir computing |
| title_sort | emerging opportunities and challenges for the future of reservoir computing |
| url | https://doi.org/10.1038/s41467-024-45187-1 |
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