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Area of Science:

  • Fluid Dynamics
  • Complex Systems
  • Computational Physics

Background:

  • Chaotic fluid flows exhibit complex, deterministic behavior.
  • Inferring these dynamics often requires extensive physical knowledge.
  • Reservoir computing offers a data-driven approach to modeling complex systems.

Purpose of the Study:

  • To infer microscopic and macroscopic behaviors of 3D chaotic fluid flow.
  • To explore data-driven inference without prior physical process knowledge.
  • To evaluate the efficacy of partial and full inference methods using reservoir computing.

Main Methods:

  • Utilized reservoir computing for inferring fluid flow dynamics.
  • Implemented partial inference requiring continuous partial time-series data.
  • Developed full inference using only past time-series data for training.
  • Employed delay coordinates to infer time-series data from single measurements.

Main Results:

  • Partial inference successfully predicted long-time microscopic fluid variables.
  • Full inference, using only energy function data, predicted future behavior and energy spectrum.
  • Inferred dynamical systems from past data were equivalent to macroscopic behavior descriptors.
  • Demonstrated accurate time-series inference from limited (single) measurements.

Conclusions:

  • Reservoir computing effectively infers complex fluid dynamics with minimal prior knowledge.
  • Full inference from energy function data is sufficient for macroscopic behavior prediction.
  • The method provides a powerful tool for understanding and predicting chaotic fluid systems.