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Related Concept Videos

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Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
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Updated: Nov 15, 2025

Forming, Confining, and Observing Microtubule-Based Active Nematics
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Machine learning active-nematic hydrodynamics.

Jonathan Colen1,2, Ming Han2,3, Rui Zhang3,4

  • 1Department of Physics, University of Chicago, Chicago, IL 60637.

Proceedings of the National Academy of Sciences of the United States of America
|March 3, 2021
PubMed
Summary
This summary is machine-generated.

Neural networks can map hydrodynamic parameters and forecast chaotic dynamics in active matter systems. This AI approach analyzes biofilament orientation to predict complex behaviors, even with incomplete experimental data.

Keywords:
active turbulencebiomaterialsdeep learningliquid crystalstopological defects

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Area of Science:

  • Physics
  • Biophysics
  • Machine Learning

Background:

  • Hydrodynamic theories describe non-equilibrium many-body systems using macroscopic parameters.
  • Determining these parameters from microscopic information is challenging, especially in active matter.
  • Active matter systems feature hydrodynamic parameters as fields reflecting energy-injecting components.

Purpose of the Study:

  • To demonstrate neural networks' capability in mapping spatiotemporal variations of hydrodynamic parameters in active nematics.
  • To forecast the chaotic dynamics of active nematic systems using machine learning.
  • To analyze biofilament/molecular-motor experiments as computer vision problems.

Main Methods:

  • Utilizing neural networks, specifically autoencoders and recurrent neural networks with residual architecture.
  • Analyzing biofilament orientation from experimental image sequences as input.
  • Treating microtubule/kinesin and actin/myosin complex experiments as computer vision tasks.

Main Results:

  • Algorithms successfully mapped spatiotemporal changes in activity and elastic moduli.
  • Hydrodynamic parameters were determined based on biofilament orientation, without needing velocity fields.
  • AI models forecasted chaotic system evolution from past image sequences, outperforming deterministic simulations with imperfect initial conditions.

Conclusions:

  • Neural networks offer a powerful tool for characterizing and controlling coupled chaotic fields in physical and biological systems.
  • This AI-driven approach enables analysis even without complete knowledge of underlying dynamics.
  • The study highlights the potential of physics-inspired machine learning for complex system analysis.