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Learning physical laws from observations of complex dynamics

The laws of physics, formulated in a compact form, are elusive for complex dynamic phenomena. However, it is now shown that, using artificial intelligence constrained by the physical Onsager principle, a custom thermodynamic description of a complex system can be constructed from the observation of its dynamical behavior.

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Fig. 1: Overall workflow, including our Stochastic OnsagerNet.

References

  1. Onsager, L. Reciprocal relations in irreversible processes. I. Phys. Rev. 37, 405–426 (1931). This article formulates a general model of near-equilibrium dynamics and derives the famous reciprocal relations.

    Article  Google Scholar 

  2. Smith, D. E. & Chu, S. Response of flexible polymers to a sudden elongational flow. Science 281, 1335–1340 (1998). This paper reports experimental findings on heterogeneity in polymer dynamics under stretching forces.

    Article  Google Scholar 

  3. Wigner, E. P. The unreasonable effectiveness of mathematics in the natural sciences. Commun. Pure Appl. Math. 13, 1–14 (1960). This article discusses the role of mathematics and its relations to the sciences.

    Article  Google Scholar 

  4. Soh, B. W., Ooi, Z.-E., Vissol-Gaudin, E., Leong, C. J. & Hippalgaonkar, K. Automated electrokinetic stretcher for manipulating nanomaterials. Lab Chip 23, 3716–3726 (2023). This paper develops automated experimentation techniques for trapping and stretching nanoscale objects.

    Article  Google Scholar 

Download references

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This is a summary of: Chen, X. et al. Constructing custom thermodynamics using deep learning. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00581-5 (2023)

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Learning physical laws from observations of complex dynamics. Nat Comput Sci 4, 9–10 (2024). https://doi.org/10.1038/s43588-023-00590-4

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