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The technique that can find a system’s state through data alone

Pinpointing the state of a complex system is tricky, especially when the underlying mathematical equations aren’t known. But a data-driven technique makes light work of it — and could even change the way that models are formulated.
  1. Brendan Keith

    1. Brendan Keith is in the Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA.

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Imagine you’re on a hill, flying a kite, and someone asks you to describe exactly what the kite is doing. “It’s over to the right, just above the treeline,” you might say, “and the wind is making it spin anticlockwise and move to the left.” This is an example of state estimation: the process of determining the true state of a complex system, typically from noisy, incomplete and often indirect observations. In a world replete with data, progress in data-driven modelling is rapid, but compatible state-estimation techniques are lagging behind. Writing in Nature, Course and Nair1 propose a state-estimation technique that enables efficient forecasts using data, without the details of any underlying model. In doing so, they offer a fresh take on the general approach to model discovery.

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Nature 622, 246-247 (2023)

doi: https://doi.org/10.1038/d41586-023-03070-x

References

  1. Course, K. & Nair, P. B. Nature 622, 261–267 (2023).

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  3. Grewal, M. S. & Andrews, A. P. IEEE Control Syst. Mag. 30, 69–78 (2010).

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  4. Yazdani, A., Lu, L., Raissi, M. & Karniadakis, G. E. PLoS Comput. Biol. 16, e1007575 (2020).

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  5. Keith, B., Khadse, A. & Field, S. E. Phys. Rev. Res. 3, 043101 (2021).

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Competing Interests

The author declares no competing interests.

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