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Invisible Hydrodynamic Sensing via Metamaterial Shells Optimized by Machine Learning.

Yajuan Li¹, Yuhong Zhou¹, Yixi Wang¹

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.)

|February 17, 2026

Summary

This summary is machine-generated.

This study introduces a novel metamaterial shell for distortion-free hydrodynamic sensing. The innovative design protects the sensor core, ensuring accurate flow-field measurements in microfluidics and beyond.

Keywords:

hydrodynamic metamaterials machine learning structure design

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Area of Science:

Fluid dynamics
Materials science
Metamaterials

Background:

Accurate flow-field sensing is vital for microfluidics, biomedicine, and environmental monitoring.
Conventional sensors distort flow due to permeability mismatch, causing data errors.

Purpose of the Study:

To develop a distortion-free hydrodynamic sensing mechanism.
To overcome limitations of conventional sensing devices in microfluidic applications.

Main Methods:

Designed a metamaterial shell creating a protected sensing core.
Utilized scattering-cancellation theory for anisotropic permeability.
Employed a deep neural network for inverse microstructure design.

Main Results:

The metashell restores the core pressure field to background levels, enabling true undisturbed pressure readings.
The sensing core is rendered 'invisible' to the external flow.
Achieved a prediction error <1% in microstructure design and reduced pressure measurement errors by 4-5 orders of magnitude.

Conclusions:

The metashell-enabled sensing scheme provides near-perfect fidelity even with significant permeability contrasts.
This theoretical-machine learning framework offers a blueprint for distortion-free hydrodynamic sensing.
The approach is extendable to thermotics, acoustics, and electromagnetics.