Deep Learning-Based Metasurface Design for Smart Cooling of Spacecraft.

Area of Science:

• Nanophotonics
• Metamaterials
• Artificial Intelligence

Background:

• Reconfigurable metasurfaces are crucial for adaptive nanophotonic applications like spacecraft thermal management.
• Current design methods for these complex structures can be time-consuming and computationally intensive.

Purpose of the Study:

• To introduce a novel, fast modeling approach for designing tunable and reconfigurable metasurface structures.
• To demonstrate the utility of this approach for creating passive adaptive cooling surfaces for spacecraft.

Main Methods:

• A convolutional deep learning network models metasurface structures as multilayer image tensors.
• Operating wavelength is incorporated as input to address dimensionality mismatches.
• A feed-forward surrogate model is integrated with pattern search optimization.

Main Results:

• The deep learning model accurately predicts metasurface response with a small training dataset.
• A patterned vanadium dioxide metasurface achieved 28% reduced coating thickness and 0.43 emissivity contrast.
• The design approach successfully generated multiple unique patterns meeting design objectives.

Conclusions:

• The proposed AI-driven design method accelerates the development of reconfigurable metasurfaces.
• This approach offers a viable solution for passive spacecraft cooling applications.
• The methodology is extensible to a broad range of nanophotonic applications.