Improving model-free prediction of chaotic dynamics by purifying the incomplete input
Despite the success of data-driven machine learning in forecasting complex nonlinear dynamics, predicting future evolution based on incomplete historical data remains challenging. Reservoir Computing (RC), a widely adopted approach, suffers from incomplete past observations since it typically requir...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2024-12-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0242605 |
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