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Space-time-modulated stochastic processes.

Massimiliano Giona1

  • 1Dipartimento di Ingegneria Chimica, Materiali e Ambiente, La Sapienza Università di Roma, Via Eudossiana 18, 00184 Roma, Italy.

Physical Review. E
|January 20, 2018
PubMed
Summary

This study introduces space-time-modulated stochastic processes for systems with finite propagation velocity. These processes feature a unique feedback loop between the perturbation and the evolving physical system, enabling applications in curved space-times.

Area of Science:

  • Physics
  • Stochastic Processes
  • General Relativity

Background:

  • Lorentzian transformations of Poisson-Kac processes in inertial frames present physical challenges.
  • Stochastic processes with finite propagation velocity require novel modeling approaches.

Purpose of the Study:

  • Introduce space-time-modulated stochastic processes for systems with finite propagation velocity.
  • Establish a framework for understanding the interplay between stochastic perturbations and evolving physical observables.
  • Extend stochastic process modeling to curved space-time manifolds.

Main Methods:

  • Definition of space-time-modulated processes incorporating nonlinear amplitude and time-horizon modulation functions.
  • Derivation of balance equations for probability density functions of modulated Poisson-Kac processes.

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  • Analysis of feedback mechanisms between stochastic perturbations and physical observables.
  • Main Results:

    • Introduced space-time-modulated stochastic processes with two-way coupling.
    • Demonstrated that these processes possess a unique feedback property crucial for curved space-time extensions.
    • Derived balance equations for modulated Poisson-Kac processes.

    Conclusions:

    • Space-time-modulated stochastic processes offer a powerful new class of models for physical systems with finite propagation velocity.
    • The inherent feedback mechanism is key to their applicability in complex space-time geometries.
    • Further analysis of specific examples reveals the unique characteristics of these processes.