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Related Experiment Video

Updated: Jun 12, 2026

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms
08:48

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Programmable Hydrodynamic Invisibility Enabled by Machine-Learning-Guided Metamaterials.

Lili Zhang1, Yiyang Zhang1, Jinrong Liu2

  • 1Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Fudan University, Shanghai, China.

Advanced Materials (Deerfield Beach, Fla.)
|June 11, 2026
PubMed
Summary

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This summary is machine-generated.

Researchers developed programmable hydrodynamic metamaterials for fluid transport control in porous media. This machine-learning-guided approach achieves robust hydrodynamic invisibility, adapting to varying background conditions for advanced applications.

Area of Science:

  • Fluid Dynamics
  • Materials Science
  • Metamaterials

Background:

  • Fluid transport in porous media is crucial for natural processes and technology.
  • Hydrodynamic invisibility cloaks manipulate flow without external disturbance but are typically static.
  • Existing devices fail with variable background permeability.

Purpose of the Study:

  • To develop a programmable hydrodynamic invisibility system adaptable to varying background permeabilities.
  • To demonstrate a machine-learning-guided metamaterial strategy for robust flow manipulation.
  • To enable hydrodynamic camouflage by matching external flow fields.

Main Methods:

  • Utilized a machine-learning-guided inverse-design framework.
  • Developed a tunable-permeability metamaterial shell for cloaking.
Keywords:
hydrodynamic metamaterialsmachine learningporous medium

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  • Employed experimental validation across diverse background permeabilities.

    • Main Results:

    • Achieved high-fidelity hydrodynamic cloaking across high, medium, and low background permeabilities.
    • Demonstrated programmable control over fluid transport.
    • Showcased the ability to match prescribed exterior reference flows for camouflage.

    Conclusions:

    • Programmable hydrodynamic metamaterials offer a scalable, general strategy for robust, on-demand fluid transport manipulation.
    • This approach overcomes limitations of static invisibility devices.
    • Potential applications span separation science, microfluidics, and biomechanics.