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The flow from simulation to reality

Nature Physics volume 18, pages 1260–1261 (2022)Cite this article

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Fluid simulations today are remarkably realistic. In this Comment I discuss some of the most striking results from the past 20 years of computer graphics research that made this happen.

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Fig. 1: Two optimizations for fluid simulations.

a, reproduced from ref. 5, ACM; b, courtesy of Chris Wojtan and Eitan Grinspun.

Fig. 2: Advanced fluid simulation effects.

a, The FLIP Fluids Addon Development Team; b, reproduced from ref. 8, ACM.

References

  1. Harlow, F. & Welch, J. E. Phys. Fluids 8, 2182–2189 (1965).

    Article  ADS  MathSciNet  Google Scholar 

  2. Foster, N. & Metaxas, D. Graph. Models Im. Proc. 58, 471–483 (1996).

    Article  Google Scholar 

  3. Stam, J. Stable fluids. In SIGGRAPH ‘99: Proc. 26th Conference on Computer Graphics and Interactive Techniques 121–128 (ACM, 1999).

  4. Berger, M. J. & Colella, P. J. Comput. Phys. 82, 64–84 (1984).

    Article  ADS  Google Scholar 

  5. Ando, R., Thürey, N. & Wojtan, C. ACM Trans. Graph. 32, 103 (2013).

    Article  Google Scholar 

  6. Da, F., Hahn, D., Batty, C., Wojtan, C. & Grinspun, E. ACM Trans. Graph. 35, 78 (2016).

    Article  Google Scholar 

  7. Ihmsen, M., Akinci, N., Akinci, G. & Teschner, M. Visual Comput. 28, 669–677 (2012).

    Article  Google Scholar 

  8. Ruan, L. et al. ACM Trans. Graph. 40, 120 (2021).

    Article  Google Scholar 

  9. Lyu, C., Li, W., Desbrun, M. & Liu, X. ACM Trans. Graph. 40, 201 (2021).

    Article  ADS  Google Scholar 

  10. Huang, L., Hädrich, T. & Michels, D. L. ACM Trans. Graph. 38, 93 (2019).

    Google Scholar 

  11. Fei, Y., Maia, H. T., Batty, C., Zheng, C. & Grinspun, E. ACM Trans. Graph. 36, 56 (2017).

    Article  Google Scholar 

  12. Takahashi, T. & Lin, M. C. ACM Trans. Graph. 38, 237 (2019).

    Article  Google Scholar 

  13. Sanchez-Gonzalez, A. et al. Learning to simulate complex physics with graph networks. Proc. 37th International Conference on Machine Learning 119, 8459–8468 (2020).

    Google Scholar 

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Acknowledgements

I thank C. Batty who graciously provided constructive feedback.

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  1. Two Minute Papers, Pécs, Hungary

    Károly Zsolnai-Fehér

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  1. Károly Zsolnai-Fehér
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Correspondence to Károly Zsolnai-Fehér.

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Zsolnai-Fehér, K. The flow from simulation to reality. Nat. Phys. 18, 1260–1261 (2022). https://doi.org/10.1038/s41567-022-01788-5

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