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Major Role of Multiscale Entropy Evolution in Complex Systems and Data Science.
Shahid Nawaz1, Muhammad Saleem2, Fedor V Kusmartsev3
1Department of Physics, Loughborough University, Loughborough LE11 3TU, UK.
Entropy (Basel, Switzerland)
|April 26, 2024
Summary
This study introduces multiscale entropy dynamics (MED) to analyze complex systems. MED reveals system drivers and expresses dynamics via a Generalized Nonlinear Schrödinger Equation (GNSE), applicable across fields.
Area of Science:
- Complex systems research
- Data science applications
- Nonlinear dynamics
Background:
- Complex systems are ubiquitous in natural and social sciences.
- Data science, AI, and machine learning offer tools to understand complex systems without deep dynamic knowledge.
Purpose of the Study:
- To demonstrate the pivotal role of multiscale entropy (MSE) in characterizing complex system steady states.
- To introduce a novel framework, multiscale entropy dynamics (MED), for dissecting system evolution and identifying driving forces.
Main Methods:
- Application of multiscale entropy (MSE) to analyze complex system steady states.
- Development and implementation of the multiscale entropy dynamics (MED) methodology.
- Mathematical modeling using a Generalized Nonlinear Schrödinger Equation (GNSE).
Main Results:
- Multiscale entropy (MSE) is confirmed as crucial for describing complex system steady states.
- The MED methodology provides a framework for dissecting system dynamics.
- Complex system dynamics can be expressed through a Generalized Nonlinear Schrödinger Equation (GNSE).
Conclusions:
- The MED methodology offers a powerful approach to understanding complex system dynamics.
- The GNSE provides a unifying mathematical expression for diverse complex system behaviors.
- This research deepens the understanding of entropic underpinnings in complex systems.
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