The stochastic nonlinear Schrödinger model (SNLSM) in (1+1)-dimension with random potential is examined in this paper. The analysis of the evolution of nonlinear dispersive waves in a totally disordered medium depends heavily on the model under investigation. This study has three main objectives. Firstly, for the SNLSM, derive stochastic precise solutions by using the modified Sardar sub-equation technique. This technique is efficient and intuitive for solving such models, as shown by the generated solutions, which can be described as trigonometric, hyperbolic, bright, single and dark. Secondly, for obtaining numerical solutions to the SNLSM, the algorithms described here offer an accurate and efficient technique. Lastly, investigate the phase plane analysis of the perturbed and unperturbed dynamical system and the time series analysis of the governing model. The results show that the numerical and analytical techniques can be extended to solve other nonlinear partial differential equations in physics and engineering. The results of this study have a significant impact on how well we comprehend how solitons behave in physical systems. Additionally, they may serve as a foundation for the development of improved numerical techniques for handling challenging nonlinear partial differential equations.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10830001 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296678 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensional latent dynamics have played a fundamental role in characterizing neural systems. Yet, what constitutes a successful method involves two opposing criteria: (1) methods should be expressive enough to capture complex nonlinear dynamics, and (2) they should maintain a notion of interpretability often only warranted by simpler linear models.
View Article and Find Full Text PDFDissipative constraint-based multi-area power system with time-varying delays and cyber-attacks.
ISA Trans
January 2025
Department of Applied Mathematics, Bharathiar University, Coimbatore 641046, India; Department of Mathematics, Sungkyunkwan University, Suwon 440746, Republic of Korea. Electronic address:
This work describes the dissipative constraint-based load frequency control problem for multi-area power system under load disturbances. Particularly, a new model incorporating time-varying delays and cyber-attacks are widespread in communication networks, significantly impacting control and stability. Consequently, the state-space equations of the addressed model are formulated and analyzed under the impact of false data injection attacks, and time-varying delays.
View Article and Find Full Text PDFMinimizing Delay and Power Consumption at the Edge.
Sensors (Basel)
January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
View Article and Find Full Text PDFNature-Inspired Metaheuristic Optimization for Control Tuning of Complex Systems.
Biomimetics (Basel)
December 2024
Institute of Knowledge Technology, Complutense University of Madrid, 28040 Madrid, Spain.
In this contribution, a methodology for the optimal tuning of controllers of complex systems based on meta-heuristic techniques is proposed. Two bio-inspired meta-heuristic optimization algorithms -the Antlion Optimizer (ALO) and the Whale Optimization Algorithm (WOA)- have been applied to two different dynamic systems: the Hoop & Ball electromechanical system, a system where a linearized description is adequate; and to a Wind Turbine-Generator-Rectifier, as an example of a complex non-linear dynamic system. The performance of the ALO and WOA techniques for the tuning of conventional PID controllers is evaluated in relation to the number of agents nS and the maximum number of iterations nMaxIter; given the stochastic nature of both methods, repeatability is also addressed.
View Article and Find Full Text PDFHeat Transport Hysteresis Generated Through Frequency Switching of a Time-Dependent Temperature Gradient.
Entropy (Basel)
December 2024
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
A stochastic energetics framework is applied to examine how periodically shifting the frequency of a time-dependent oscillating temperature gradient affects heat transport in a nanoscale molecular model. We specifically examine the effects that frequency switching, i.e.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!