Physics-informed neural networks (PINNs) for fluid mechanics: a review
2021 · Acta Mechanica Sinica · 1,558 citations
https://doi.org/10.1007/s10409-021-01148-1
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Shengze Cai
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Shengze Cai is an academic researcher from Macau University of Science and Technology. The author has contributed to research in topics: Model Reduction and Neural Networks & Fluid Dynamics and Turbulent Flows & Advanced Image Processing Techniques. The author has an h-index of 20, co-authored 62 publications.
ORCID: 0000-0003-0122-6864Papers by this author
worksNSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
2020 · Journal of Computational Physics · 1,065 citations
https://doi.org/10.1016/j.jcp.2020.109951
Physics-Informed Neural Networks for Heat Transfer Problems
2021 · Journal of Heat Transfer · 1,017 citations
https://doi.org/10.1115/1.4050542
Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks
2021 · Journal of Fluid Mechanics · 312 citations
https://doi.org/10.1017/jfm.2021.135
Heat Transfer Prediction With Unknown Thermal Boundary Conditions Using Physics-Informed Neural Networks
2020 · 36 citations
https://doi.org/10.1115/fedsm2020-20159
Accelerated optimization in deep learning with a proportional-integral-derivative controller
2024 · Nature Communications · 22 citations
https://doi.org/10.1038/s41467-024-54451-3
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