arXiv subjects

Moein Khalighi

Publications and source records attributed to Moein Khalighi.

12 recordsLinked to original sources

arXiv2026

Memory reshapes stability landscapes: resilience-resistance tradeoffs and critical transitions

Regime shifts in biology, ecology, and other complex systems are often interpreted through stability landscapes and early warning signals that implicitly assume dynamics without memory effects. Yet many real systems exhibit these effects, thus present dynamics depend on past states and past forcing. Here, we study how memory reshapes bistable stability landscapes and regime shifts using a minimal bistable model with a fractional derivative that controls memory strength. We connect landscape geometry to classical notions of resilience and resistance by quantifying basin curvature and the perturbation magnitude required to cross the unstable threshold, and we track how these quantities evolve in time after perturbations. Memory typically flattens basin floors, slowing recovery, while often increasing the perturbation threshold for stability transitions, revealing a tradeoff between resilience and resistance. Because the landscape becomes history-dependent and time-evolving, memory generates qualitative behaviors that do not appear in memory-free models, including delayed collapse or recovery after stress ends, rebound after apparently successful transition, and broadened hysteresis under gradual parameter change. Finally, we show that fitting a memory-free model to memory-driven data can reproduce trajectories while systematically shifting equilibrium branches and tipping locations, risking incorrect diagnosis and management of regime shifts. These results motivate a moving landscape view and provide practical guidance for interpreting observed anomalies and distinguishing memory-driven effects from noise.

math.DS↗

arXiv2026

LDG method for solving spatial and temporal fractional nonlinear convection-diffusion equations

This paper focuses on a nonlinear convection-diffusion equation with space and time-fractional Laplacian operators of orders $1<β<2$ and $0<α\leq1$, respectively. We develop local discontinuous Galerkin methods, including Legendre basis functions, for a solution to this class of fractional diffusion problem, and prove stability and optimal order of convergence $O(h^{k+1}+(Δt)^{1+\frac{p}{2}}+p^2)$. This technique turns the equation into a system of first-order equations and approximates the solution by selecting the appropriate basis functions. Regarding accuracy and stability, the basis functions greatly improve the method. According to the numerical results, the proposed scheme performs efficiently and accurately in various conditions and meets the optimal order of convergence.

math.NA↗

arXiv2025

Stability of Fractional-Order Discrete-Time Systems with Application to Rulkov Neural Networks and Asymmetric Memristor Synapses

Memristors have emerged as ideal components for modeling synaptic connections in neural networks due to their ability to emulate synaptic plasticity and memory effects. Discrete models of memristor-coupled neurons are crucial for simplifying computations and efficiently analyzing large-scale neural networks. Furthermore, incorporating fractional-order calculus into discrete models enhances their capacity to capture the memory and hereditary properties inherent in biological neurons, thus reducing numerical discretization errors compared to integer-order models. Despite this potential, discrete fractional-order neural models coupled through memristors have received limited attention. To address this gap, we introduce two novel discrete fractional-order neural systems. The first system consists of two Rulkov neurons coupled via dual memristors to emulate synaptic functions. The second system expands this configuration into a ring-shaped network of neurons consisting of multiple similar subnetworks. We present a novel theorem that defines stability regions for discrete fractional-order systems, applicable to both proposed models. Integrating discrete fractional-order calculus into memristor-coupled neural models provides a foundation for more accurate and efficient simulations of neural dynamics. This work advances the understanding of neural network stability and paves the way for future research into efficient neural computations.

q-bio.NC↗

arXiv2025

Reconstructing ecological community dynamics from limited observations

Ecosystems tend to fluctuate around stable equilibria in response to internal dynamics and environmental factors. Occasionally, they enter an unstable tipping region and collapse into an alternative stable state. Our understanding of how ecological communities vary over time and respond to perturbations depends on our ability to quantify and predict these dynamics. However, the scarcity of long, dense time series data poses a severe bottleneck for characterising community dynamics using existing methods. We overcome this limitation by combining information across multiple short time series using Bayesian inference. By decomposing dynamics into deterministic and stochastic components using Gaussian process priors, we predict stable and tipping regions along the community landscape and quantify resilience while addressing uncertainty. After validation with simulated and real ecological time series, we use the model to question common assumptions underlying classical potential analysis and re-evaluate the stability of previously proposed "tipping elements" in the human gut microbiota.

stat.AP↗

arXiv2025

Fractional modelling of COVID-19 transmission incorporating asymptomatic and super-spreader individuals

The COVID-19 pandemic has presented unprecedented challenges worldwide, necessitating effective modelling approaches to understand and control its transmission dynamics. In this study, we propose a novel approach that integrates asymptomatic and super-spreader individuals in a single compartmental model. We highlight the advantages of utilizing incommensurate fractional order derivatives in ordinary differential equations, including increased flexibility in capturing disease dynamics and refined memory effects in the transmission process. We conduct a qualitative analysis of our proposed model, which involves determining the basic reproduction number and analysing the disease-free equilibrium's stability. By fitting the proposed model with real data from Portugal and comparing it with existing models, we demonstrate that the incorporation of supplementary population classes and fractional derivatives significantly improves the model's goodness of fit. Sensitivity analysis further provides valuable insights for designing effective strategies to mitigate the spread of the virus.

q-bio.PE↗

arXiv2024

Epidemic Transmission Modeling with Fractional Derivatives and Environmental Pathogens

This research presents an advanced fractional-order compartmental model designed to delve into the complexities of COVID-19 transmission dynamics, specifically accounting for the influence of environmental pathogens on disease spread. By enhancing the classical compartmental framework, our model distinctively incorporates the effects of order derivatives and environmental shedding mechanisms on the basic reproduction numbers, thus offering a holistic perspective on transmission dynamics. Leveraging fractional calculus, the model adeptly captures the memory effect associated with disease spread, providing an authentic depiction of the virus's real-world propagation patterns. A thorough mathematical analysis confirming the existence, uniqueness, and stability of the model's solutions emphasizes its robustness. Furthermore, the numerical simulations, meticulously calibrated with real COVID-19 case data, affirm the model's capacity to emulate observed transmission trends, demonstrating the pivotal role of environmental transmission vectors in shaping public health strategies. The study highlights the critical role of environmental sanitation and targeted interventions in controlling the pandemic's spread, suggesting new insights for research and policy-making in infectious disease management.

q-bio.PE↗

arXiv2022

FdeSolver: A Julia Package for Solving Fractional Differential Equations

Implementing and executing numerical algorithms to solve fractional differential equations has been less straightforward than using their integer-order counterparts, posing challenges for practitioners who wish to incorporate fractional calculus in applied case studies. Hence, we created an open-source Julia package, FdeSolver, that provides numerical solutions for fractional-order differential equations based on product-integration rules, predictor-corrector algorithms, and the Newton-Raphson method. The package covers solutions for one-dimensional equations with orders of positive real numbers. For high-dimensional systems, the orders of positive real numbers are limited to less than (and equal to) one. Incommensurate derivatives are allowed and defined in the Caputo sense. Here, we summarize the implementation for a representative class of problems, provide comparisons with available alternatives in Julia and Matlab, describe our adherence to good practices in open research software development, and demonstrate the practical performance of the methods in two applications; we show how to simulate microbial community dynamics and model the spread of Covid-19 by fitting the order of derivatives based on epidemiological observations. Overall, these results highlight the efficiency, reliability, and practicality of the FdeSolver Julia package.

math.NA↗

arXiv2021

Bifurcation analysis of a fractional-order Pinsky-Rinzel model

Abstract The present work describes a new fractional-order system of a two-compartment CA3 hippocampal pyramidal cell, which is known as Pinsky-Rinzel model with Caputo fractional derivative. Firstly, The transient of the solutions is investigated. Then based on the bifurcation diagrams, we study the general behavior of the system. In this case, fractional derivative order and currents injection, are taken as bifurcation parameters. Chaotic regions are obtained for different values of the fractional derivative order and different injection currents. Finally, a numerical approach is introduced to study the stability of the system under certain conditions. The obtained results can be considered as help to control relevant diseases caused by maximal injection currents abnormality.

math.DS↗

arXiv2020

Three-species predator-prey model with respect to Caputo and Caputo-Fabrizio fractional operators

We study distributed lag effects in three-dimensional Lotka-Volterra systems by applying the concept of fractional calculus. We derive a new numerical method that provides enhanced stability for the Caputo-Fabrizio operator based on Adams-Bashforth method, considering non-singular kernel in the definition of Caputo-Fabrizio operator. We investigate the stability conditions of this system with comparisons to the Caputo fractional derivative. Numerical results show that the type of differential operators and the value of orders significantly influence the stability of the numerical solution, and dynamics of the Lotka-Volterra system.

math.NA↗

arXiv2020

The strategy of survival for a competition between normal and anomalous diffusion

In this paper, we study the competition of two diffusion processes for achieving the maximum possible diffusion in an area. This competition, however, does not occur in the same circumstance; one of these processes is a normal diffusion with a higher growth rate, and another one is an anomalous diffusion with a lower growth rate. The trivial solution of the proposed model suggests that the winner is the one with the higher growth rate. But, the question is: what characteristics and strategies should the second diffusion include to prolong the survival in such a competition? The studied diffusion equations correspond to the SI model such that the anomalous diffusion has memory described by a fractional order derivative. The strategy promise that anomalous diffusion reaches maximum survival in case of forgetting some parts of the memory. This model can represent some of real phenomena, such as the contest of two companies in a market share, the spreading of two epidemic diseases, the diffusion of two species, or any reaction-diffusion related to real-world competition.

physics.soc-ph↗

arXiv2020

A new approach to solving multi-order fractional equations using BEM and Chebyshev matrix

In this paper, the boundary element method is combined with Chebyshev operational matrix technique to solve two-dimensional multi-order time-fractional partial differential equations; nonlinear and linear in respect to spatial and temporal variables, respectively. Fractional derivatives are estimated by Caputo sense. Boundary element method is used to convert the main problem into a system of a multi-order fractional ordinary differential equation. Then, the produced system is approximated by Chebyshev operational matrix technique, ans its condition number is analyzed. Accuracy and efficiency of the proposed hybrid scheme are demonstrated by solving three different types of two-dimensional time fractional convection-diffusion equations numerically. The convergent rates are calculated for different meshing within the boundary element technique. Numerical results are given by graphs and tables for solutions and different type of error norms.

math.AP↗

arXiv2017

Fractional Dynamics of Network Growth Constrained by aging Node Interactions

In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of "commonly accepted beliefs" seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabasi-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node's age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, - even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model.

physics.soc-ph↗