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  3. Optomechanical Reservoir Computing.
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  3. Optomechanical Reservoir Computing.

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Optomechanical reservoir computing.

Steven Kiyabu1, Daniel Nelson1, John Thomson1

  • 1UES, a BlueHalo Company, Advanced Research and Development Division, Dayton, OH 45432.

Proceedings of the National Academy of Sciences of the United States of America
|July 14, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel optomechanical reservoir computer that combines nonlinear springs and optical fibers. This multiphysics design enhances nonlinear frequency content for improved physical reservoir computing performance.

Keywords:
analog computingembodied intelligencephysical computationreservoir computingspectral analysis

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Area of Science:

  • Physics
  • Engineering
  • Computer Science

Background:

  • Nonlinear dynamics are essential for physical reservoir computing, enabling complex input-output mappings.
  • Existing physical reservoirs often rely on a single source of nonlinearity, limiting their predictive capabilities.
  • A gap exists between theoretical analysis and practical evaluation of reservoir computer performance.

Purpose of the Study:

  • To introduce a novel optomechanical reservoir that integrates multiple sources of nonlinearity.
  • To analyze and characterize the nonlinear frequency content generated by the reservoir.
  • To advance the rational design of physical reservoir computers for embodied intelligence.

Main Methods:

  • Developed an optomechanical reservoir combining nonlinear springs and nonlinear optical fiber sensing.
  • Employed a novelty search on simulated reservoirs to identify high-performing designs.
  • Validated promising designs through experimental testing.
  • Introduced a frequency content metric to characterize reservoir nonlinearity.

    • Main Results:

    • The combined nonlinear springs and optical fibers significantly expanded the reservoir's nonlinear frequency content.
    • Novelty search identified and experimental validation confirmed high-performing optomechanical reservoir designs.
    • The frequency content metric effectively characterized the reservoir's nonlinear capabilities.

    Conclusions:

    • The optomechanical reservoir, leveraging multiphysics nonlinearity, demonstrates enhanced performance for physical reservoir computing.
    • The developed analytical techniques and multiphysics designs pave the way for more effective embodied intelligence.
    • This work bridges the gap between fundamental reservoir capabilities and practical applications.