Machine learning from limited data: Predicting biological dynamics under a time-varying external input
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913499956903936 |
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| author | Kang, Hoony Srinivasan, Keshav Losert, Wolfgang |
| author_facet | Kang, Hoony Srinivasan, Keshav Losert, Wolfgang |
| contents | Reservoir computing (RC) is known as a powerful machine learning approach for learning complex dynamics from limited data. Here, we use RC to predict highly stochastic dynamics of cell shapes. We find that RC is able to predict the steady state climate from very limited data. Furthermore, the RC learns the timescale of transients from only four observations. We find that these capabilities of the RC to act as a dynamic twin allows us to also infer important statistics of cell shape dynamics of unobserved conditions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_07998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning from limited data: Predicting biological dynamics under a time-varying external input Kang, Hoony Srinivasan, Keshav Losert, Wolfgang Biological Physics Computational Physics Data Analysis, Statistics and Probability Reservoir computing (RC) is known as a powerful machine learning approach for learning complex dynamics from limited data. Here, we use RC to predict highly stochastic dynamics of cell shapes. We find that RC is able to predict the steady state climate from very limited data. Furthermore, the RC learns the timescale of transients from only four observations. We find that these capabilities of the RC to act as a dynamic twin allows us to also infer important statistics of cell shape dynamics of unobserved conditions. |
| title | Machine learning from limited data: Predicting biological dynamics under a time-varying external input |
| topic | Biological Physics Computational Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2408.07998 |