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A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems

Luca Menicali et al.

Journal of the American Statistical Association2026https://doi.org/10.1080/01621459.2026.2632865article
AJG 4ABDC A*
Weight
0.50

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https://doi.org/https://doi.org/10.1080/01621459.2026.2632865

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@article{luca2026,
  title        = {{A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems}},
  author       = {Luca Menicali et al.},
  journal      = {Journal of the American Statistical Association},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/01621459.2026.2632865},
}

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A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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