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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Autonomous inference of complex network dynamics from incomplete and noisy data.

Ting-Ting Gao1,2, Gang Yan3,4,5

  • 1MOE Key Laboratory of Advanced Micro-Structured Materials and School of Physics Science and Engineering, Tongji University, Shanghai, People's Republic of China.

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|January 4, 2024
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This summary is machine-generated.

Researchers developed a novel computational method to uncover the dynamics of complex networks from data. This approach accurately infers system behaviors, even with noisy or incomplete information, offering insights into various real-world systems.

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Area of Science:

  • Network Science
  • Computational Biology
  • Systems Dynamics

Background:

  • Increasing availability of empirical data for complex networked systems.
  • Lack of versatile computational tools to infer nodal and interaction dynamics from data.

Purpose of the Study:

  • To develop a computational toolbox for autonomous inference of complex network dynamics.
  • To demonstrate the effectiveness and robustness of the developed approach.

Main Methods:

  • A two-phase approach for autonomous inference of complex network dynamics.
  • Testing on synthetic and real networks including neuronal, genetic, social, and coupled oscillator systems.

Main Results:

  • Successful inference of dynamics across diverse network types.
  • Demonstrated robustness to various forms of data incompleteness and noise.
  • Inferred early spreading dynamics of Influenza A accurately predicted other disease outbreaks like COVID-19.

Conclusions:

  • The developed two-phase approach provides a versatile tool for analyzing complex network dynamics.
  • The method's robustness makes it applicable to real-world, often noisy, datasets.
  • Offers a pathway to discover hidden microscopic mechanisms in a wide range of networked systems.