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Learning transport processes with machine intelligence.

Francesco Miniati1, Gianluca Gregori2

  • 1Department of Physics, University of Oxford, Parks Road, Oxford, OX1 3PU, UK. francesco.miniati@physics.ox.ac.uk.

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Summary
This summary is machine-generated.

Machine learning models bridge micro-physics and macro-scale modeling by creating transport flux representations. This approach addresses noise issues in deep neural networks, improving numerical simulations for plasma physics.

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Area of Science:

  • Computational physics
  • Plasma physics
  • Machine learning

Background:

  • Complex micro-physics in transport processes are difficult to model theoretically.
  • Microscopic simulations and experiments contain valuable data not suitable for macroscopic modeling.
  • Bridging the gap between micro and macro scales is crucial for accurate simulations.

Purpose of the Study:

  • To develop machine learning methods for micro-physics informed transport flux representations.
  • To address the noisiness of deep neural networks in numerical schemes.
  • To demonstrate the methodology's capability in modeling heat flux suppression.

Main Methods:

  • Utilizing machine learning to derive macroscopic transport flux representations from microscopic data.
  • Developing a methodology to mitigate deep neural network noise for second-order convergent schemes.
  • Applying an advanced symbolic regression tool for comparison.

Main Results:

  • Successfully deployed micro-physics informed transport flux representations for continuum mechanics.
  • Presented a noise-mitigation methodology for deep neural networks in numerical schemes.
  • Demonstrated effectiveness in modeling heat flux suppression in fusion and cosmic plasmas.
  • Symbolic regression offered accurate and usable representations for numerical analysis.

Conclusions:

  • Machine learning offers a viable approach to integrate micro-physics into macroscopic models.
  • The developed methodology enhances the stability and performance of numerical simulations.
  • This work provides a promising step towards bridging micro- and macro-scale modeling in physics.
  • Symbolic representations show potential for accuracy and ease of use in theoretical analysis.