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This study introduces a hybrid machine learning approach combining data-driven methods with physical principles to discover new physics from complex experimental data, successfully modeling turbulent fluid flow and reconstructing unmeasured variables.

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Area of Science:

  • Physics
  • Fluid Dynamics
  • Machine Learning

Background:

  • Traditional physics discovery relies on first-principle analysis.
  • Purely data-driven machine learning struggles with noisy, high-dimensional, and incomplete scientific data.
  • Existing methods are limited to simple, low-dimensional systems.

Purpose of the Study:

  • To develop a hybrid methodology integrating machine learning with physical principles.
  • To discover a quantitatively accurate model for complex non-equilibrium systems from experimental data.
  • To reconstruct inaccessible physical variables from accessible measurements.

Main Methods:

  • A hybrid approach combining data-driven machine learning with general physical principles.
  • Application to experimental data from a weakly turbulent fluid flow.
  • Focus on reconstructing the velocity field to infer pressure and forcing fields.

Main Results:

  • Successfully developed a quantitatively accurate model for a non-equilibrium, spatially extended system.
  • Demonstrated the method's efficacy on high-dimensional, noisy, and incomplete experimental data.
  • Reconstructed inaccessible variables (pressure and forcing fields) from the accessible velocity field.

Conclusions:

  • Hybrid machine learning approaches can overcome limitations of purely data-driven methods in physics discovery.
  • This hybrid method enables accurate modeling of complex fluid dynamics from limited experimental data.
  • The approach facilitates the reconstruction of unmeasured physical quantities, advancing experimental data analysis.