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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Dynamical system analysis of a data-driven model constructed by reservoir computing.

Miki U Kobayashi1, Kengo Nakai2, Yoshitaka Saiki3

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Summary
This summary is machine-generated.

Data-driven models precisely reconstruct chaotic fluid flow dynamics. This approach enables predictions of laminar duration, overcoming computational limits of direct simulations.

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Area of Science:

  • Dynamical Systems Theory
  • Computational Fluid Dynamics
  • Data-Driven Modeling

Background:

  • Chaotic fluid flow exhibits complex dynamics.
  • Direct numerical simulation of Navier-Stokes equations is computationally expensive.
  • Understanding dynamical characteristics is crucial for fluid flow analysis.

Purpose of the Study:

  • Evaluate data-driven models from a dynamical system perspective.
  • Precisely reconstruct dynamical characteristics of chaotic systems.
  • Predict macroscopic variables in chaotic fluid flow.

Main Methods:

  • Analysis of dynamical system properties (fixed points, orbits, saddles, Lyapunov exponents, manifolds).
  • Reconstruction of dynamical characteristics using data-driven models.
  • Prediction of laminar lasting time distribution for chaotic fluid flow.

Main Results:

  • Data-driven models offer more precise reconstruction of dynamical characteristics compared to direct computation.
  • Dynamical features like unstable fixed points and chaotic saddles are accurately captured.
  • The study successfully predicts laminar lasting time distribution.

Conclusions:

  • Data-driven modeling provides a powerful alternative for analyzing complex dynamical systems.
  • This method overcomes computational barriers in simulating high-cost fluid dynamics.
  • Accurate prediction of macroscopic variables is achievable, advancing fluid flow research.