Novel Approach Based Minimization of Geometric Action for Predicting Rare and Extreme Events in Non-Equilibrium Systems

Authors P.M. Patare1, P.B. Khatkale2, A.A. Khatri3, P.M. Yawalkar4, V.M. Tidake5 , S.S. Ingle1, M.V. Kulkarni6
Affiliations

1Department of Mechanical Engineering, Sanjivani College of Engineering Kopargaon, SPPU, Pune, India

2Sanjivani University, Kopargaon, MH, India

3Department of Computer Engineering, Jaihind College of Engineering, Kuran, SPPU, Pune, MH, India

4Department of Computer Engineering, MET's Institute of Engineering, Nashik, India

5Department of MBA, Sanjivani College of Engineering, Kopargaon, SPPU, Pune, India

6Engineering Science and Humanities, Sanjivani College of Engineering, Kopargaon, SPPU, Pune, India
Е-mail praveenkhatkale@gmail.com
Issue Volume 16, Year 2024, Number 4
Dates Received 15 April 2024; revised manuscript received 20 August 2024; published online 27 August 2024
Citation P.M. Patare, P.B. Khatkale, et al., J. Nano- Electron. Phys. 16 No 4, 04008 (2024)
DOI https://doi.org/10.21272/jnep.16(4).04008
PACS Number(s) 07.05.Tp
Keywords Rare and extreme events, Information length, Information flow.
Annotation

Identifying and quantifying unexpected events in non-equilibrium systems is critical work that is necessary for systems managers to make well-informed decisions, particularly when forecasting rare and extreme events. In this paper neural networks are integrated to increase the predictive capacity of information theory. Two information theory techniques, “Information Length (IL) and Information Flow (IF)”, are being examined for their sensitivity to rapid changes. To simulate the first occurrence of extreme and rare events, we utilize a non-autonomous Kramer model to introduce a perturbation. we introduced a Dynamic Osprey Long Short-Term Memory (DOLSTM) for predicting rare and extreme events in non-equilibrium systems. Our results show that IL performs better than IF in accurately forecasting unexpected occurrences when combined with a neural network. This study highlights a novel integration between information theory & neural networks, giving an effective strategy for forecasting rare & extreme events in non-equilibrium environments. An effective methodology for identifying and forecasting the behavior of dynamic systems is established by combining information-length diagnostics with neural network predictions, especially in situations involving rare and extreme events. This novel method illustrates that the theory of information and neural networks can be used to provide robust predictions for dynamic systems, when encountering rare and extreme events.

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