Bifurcation Analysis Research Articles

Pulmonary epithelial wound healing and the immune system. Mathematical modelling and bifurcation analysis of a bistable system

Respiratory diseases such as asthma, acute respiratory distress syndrome (ARDS), influenza or COVID-19 often directly target the epithelium. Elevated immune levels and a ‘cytokine storm’ are directly associated with defective healing dynamics of lung diseases such as COVID-19 or ARDS. The infected cells leave wounded regions in the epithelium which must be healed for the lung to return to a healthy state and carry out its main function of gas-exchange. Due to the complexity of the various interactions between cells of the lung epithelium and surrounding tissue, it is necessary to develop models that can complement experiments to fully understand the healing dynamics. In this mathematical study we model the mechanism of epithelial regeneration. We assume that healing is exclusively driven by progenitor cell proliferation, induced by a chemical activator such as epithelial growth factor (EGF) and cytokines such as interleukin-22 (IL22). Contrary to previous studies of wound healing, we consider the immune system, specifically the T effector cells TH1, TH17, TH22 and Treg to strongly contribute to the healing process, by producing IL22 or regulating the immune response. We therefore obtain a coupled system of two ordinary differential equations for the epithelial and immune cell densities and two functions for the levels of chemicals that either induce epithelial proliferation or recruit immune cells. These functions link the two cell equations. We find that to allow the epithelium to regenerate to a healthy state, the immune system must not exceed a threshold value at the onset of the healing phase. This immune threshold is supported experimentally but was not explicitly built into our equations. Our assumptions are therefore sufficient to reproduce experimental results concerning the ratio TH17/Treg cells as a threshold to predict higher mortality rates in patients. This immune threshold can be controlled by parameters of the model, specifically the base-level growth factor concentration. This conclusion is based on a mathematical bifurcation analysis and linearization of the model equations. Our results suggest treatment of severe cases of lung injury by reducing or suppressing the immune response, in an individual patient, assessed by their disease parameters such as course of lung injury and immune response levels.

Unraveling the dynamics of firing patterns for neurons with impairment of sodium channels.

Various factors such as mechanical trauma, chemical trauma, local ischemia, and inflammation can impair voltage-gated sodium channels (Nav) in neurons. These impairments lead to a distinctive leftward shift in the activation and inactivation curves of voltage-gated sodium channels. The resulting sodium channel impairments in neurons are known to affect firing patterns, which play a significant role in neuronal activities within the nervous system. However, the underlying dynamic mechanism for the emergence of these firing patterns remains unclear. In this study, we systematically investigated the effects of sodium channel dysfunction on individual neuronal dynamics and firing patterns. By employing codimension-1 bifurcation analysis, we revealed the underlying dynamical mechanism responsible for the generation of different firing patterns. Additionally, through codimension-2 bifurcation analysis, we theoretically determined the distribution of firing patterns on different parameter planes. Our results indicate that the firing patterns of impaired neurons are regulated by multiple parameters, with firing pattern transitions caused by the degree of sodium channel impairment being more diverse than those caused by the ratio of impaired sodium channel and current. Furthermore, we observed that the firing pattern of tonic firing is more likely to be the norm in impaired sodium channel neurons, providing valuable insights into the signaling of impaired neurons. Overall, our findings highlight the intricate relationships among sodium channel impairments, neuronal dynamics, and firing patterns, shedding light on the impact of disruptions in ion concentration gradients on neuronal function.

A dynamic analysis of a tourism-based socioecological system

The study proposes a dynamic analysis regarding interactions between the resources provided by the forest, the wildlife present inside or in the proximity of that environment and visitors revolving around the before mentioned socioecological framework. The mathematical model is described by a nonlinear system with three differential equations. The discrete time delay is introduced to illustrate the entire past impact of tourists on forest resources and wildlife. The basic assumption is that the wildlife species which inhabit the area are relying entirely on forest resources to meet their needs for food, shelter, and to attract tourists. Also, there is a positive correlation between ecotourism activities and the presence of forest resources and wildlife. The equilibrium states are determined, and they are subjected to a stability and bifurcation analysis. The study employs a Hopf bifurcation analysis in the neighborhood of the equilibrium states by choosing the time delay as the bifurcation parameter. The critical values of the time-delay that lead to oscillatory behavior are determined. Numerical simulations are carried out to show the system’s qualitative behavior in the vicinity of the equilibria.

Chatter stability analysis for non-circular high-speed grinding process with dynamic force modelling

To avoid instability and resulting chatter during the non-circular grinding process, it is necessary to elucidate the potential occurrence of periodic chatter behavior when high-speed grinding loses stability and to define the stability boundary of the grinding system. Building upon an analysis of the geometric kinematic characteristics of non-circular profiled components, a dynamic grinding force model for non-circular high-speed grinding was derived by considering both the delay effect and the elastic yielding mechanism between the grinding wheel and workpiece. Then, A multi-factor coupled dynamic model of non-circular grinding was established. By employing a multi-scale approach, bifurcation analysis and classification of the instability process in the grinding system were conducted, using a typical non-circular profiled shaft component, such as a camshaft, for theoretical analysis and experimental validation. The research determined the stability boundary of the high-speed grinding process for non-circular profile components, validated the possibility of the existence of a conditionally stable chatter region in the non-circular high-speed grinding process, and identified differences in grinding chatter characteristics among different segments of the cam profile, with a greater propensity for chatter at the juncture of the cam fall and base circle.

Learning the latent dynamics of fluid flows from high-fidelity numerical simulations using parsimonious diffusion maps

We use parsimonious diffusion maps (PDMs) to discover the latent dynamics of high-fidelity Navier–Stokes simulations with a focus on the two-dimensional (2D) fluidic pinball problem. By varying the Reynolds number Re, different flow regimes emerge, ranging from steady symmetric flows to quasi-periodic asymmetric and chaos. The proposed non-linear manifold learning scheme identifies in a crisp manner the expected intrinsic dimension of the underlying emerging dynamics over the parameter space. In particular, PDMs estimate that the emergent dynamics in the oscillatory regime can be captured by just two variables, while in the chaotic regime, the dominant modes are three as anticipated by the normal form theory. On the other hand, proper orthogonal decomposition/principal component analysis (POD/PCA), most commonly used for dimensionality reduction in fluid mechanics, does not provide such a crisp separation between the dominant modes. To validate the performance of PDMs, we also compute the reconstruction error, by constructing a decoder using geometric harmonics (GHs). We show that the proposed scheme outperforms the POD/PCA over the whole Re number range. Thus, we believe that the proposed scheme will allow for the development of more accurate reduced order models for high-fidelity fluid dynamics simulators, relaxing the curse of dimensionality in numerical analysis tasks such as bifurcation analysis, optimization, and control.

On coupled oscillators modeling bio-inspired acoustic sensors: Bifurcation analysis toward tunability enhancement.

Oscillators exhibiting an Andronov-Hopf bifurcation are candidates to mimic the functionality of the cochlea, since the transfer response of these oscillators is compressive and frequency selective. The former implies that small stimuli are amplified and strong stimuli are attenuated, while the latter means that the oscillator only reacts in a (small) frequency band. However, this implies that many oscillators are needed to cover a relevant frequency band. By introducing the notion of tunable characteristic frequencies, i.e., the characteristic frequency can be adjusted by a controllable input, the number of oscillators can be eventually reduced. Subsequently, the tunability enhancement of coupled oscillators is investigated by analyzing the local dynamics of a network of oscillators. For this, necessary conditions for the emergence of Andronov-Hopf bifurcations are determined for networks consisting of two groups, i.e., a group is a network of identical oscillators. By choosing the eigenvalues of the product of the cross-coupling matrix as bifurcation parameters and exploiting the structure of the transfer matrix of this network, the critical points and, thus, the characteristic frequency at this point can be derived. Tunability of the characteristic frequency is then enabled by controlling the asymmetry between the groups of oscillators.

Electron-acoustic solitary waves via suprathermal populations in Saturn's magnetosphere: Bifurcation, sensitivity, and stability analysis

Cassini and Voyager space missions observed non-thermal electron populations (with varying characteristics) in Saturn's magnetosphere, which can be correctly described using kappa distributions. Based on these observations, our objective is to inspect the evolution of electron-acoustic solitary waves (EASWs) within Saturn's magnetosphere. The propagation of weakly nonlinear (EASWs) in a collisional plasma system comprising a cold electron fluid, hot electrons following a kappa distribution, and stationary ions is investigated. By employing the reductive perturbation technique, the Korteweg–de Vries Burgers (KdV–B) equation is derived. An exact solution of the KdV–B equation, with a conformable time-derivative, is found using the unified method. It is observed that plasma current-induced collision between electrons and ions leads to remarkable dissipation, generating EASWs. Furthermore, when studying the sensitivity of the system, the appearance of a positive potential is depicted as external forces vanish, which may be due to stationary ions. Additionally, bifurcation, stability, and significant influence of plasma characteristics are considered.

Dynamical Study of Nonlinear Fractional-order Schrödinger Equations with Bifurcation, Chaos and Modulation Instability Analysis

Dynamical Study of Nonlinear Fractional-order Schrödinger Equations with Bifurcation, Chaos and Modulation Instability Analysis

Uncertainty quantification analysis of bifurcations of the Allen–Cahn equation with random coefficients

In this work we consider the Allen–Cahn equation, a prototypical model problem in nonlinear dynamics that exhibits bifurcations corresponding to variations of a deterministic bifurcation parameter. Going beyond the state-of-the-art, we introduce a random coefficient in the linear reaction part of the equation, thereby accounting for random, spatially-heterogeneous effects. Importantly, we assume a spatially constant, deterministic mean value of the random coefficient. We show that this mean value is in fact a bifurcation parameter in the Allen–Cahn equation with random coefficients. Moreover, we show that the bifurcation points and bifurcation curves become random objects. We consider two distinct modelling situations: (i) for a spatially homogeneous coefficient we derive analytical expressions for the distribution of the bifurcation points and show that the bifurcation curves are random shifts of a fixed reference curve; (ii) for a spatially heterogeneous coefficient we employ a generalized polynomial chaos expansion to approximate the statistical properties of the random bifurcation points and bifurcation curves. We present numerical examples in 1D physical space, where we combine the popular software package Continuation Core and Toolboxes (COCO) for numerical continuation and the Sparse Grids Matlab Kit for the polynomial chaos expansion. Our exposition addresses both, dynamical systems and uncertainty quantification, highlighting how analytical and numerical tools from both areas can be combined efficiently for the challenging uncertainty quantification analysis of bifurcations in random differential equations.

Bifurcation Analysis of Single-Bay Supersonic Panels Using Preflutter Output Data

This paper investigates an output-based approach for flutter bifurcation analysis of single-bay panels in supersonic flow. The approach leverages bifurcation forecasting, a class of methods to predict bifurcation diagrams using prebifurcation output data. This work is the first study into this approach applied to panel limit-cycle oscillations, building on previous efforts focused on geometrically nonlinear wings and propeller–nacelle systems. The study uses output data from transient simulations of single-bay panels at as few as two preflutter dynamic pressures, which are selected using an eigenvalue-based criterion that ensures consistent prediction accuracy across panel configurations. The approach captures the bifurcation type and amplitude variation of limit-cycle oscillations around the flutter point for a variety of materials, boundary conditions, thermal loads, and cross-stream curvatures. This approach can facilitate nonintrusive panel limit-cycle oscillation analyses for parametric studies and design.

Propagation of wave insights to the Chiral Schrödinger equation along with bifurcation analysis and diverse optical soliton solutions

In this study, the modified Sardar sub-equation method is capitalised to secure soliton solutions to the (1+1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(1+1)$$\\end{document}-dimensional chiral nonlinear Schrödinger (NLS) equation. Chiral soliton propagation in nuclear physics is an extremely attractive field because of its wide applications in communications and ultrafast signal routing systems. Additionally, we perform bifurcation analysis to gain a deeper understanding of the dynamics of the chiral NLS equation. This highlights the complex behaviour of the system and exposes the conditions under which various types of bifurcations occur. Additionally, a sensitivity analysis is performed to assess how small changes in initial conditions and parameters influence the solutions, offering valuable perspectives on the stability and dependability of the acquired solutions. By employing the above-mentioned methodology, we derive a variety of exact solutions, including periodic, singular, dark, bright, mixed trigonometric, exponential, hyperbolic, and rational wave solutions. The study’s findings advance our theoretical knowledge of chiral NLS equations and have potential applications in optical communication and related fields.

Mathematical model for IP$_{3}$ dependent calcium oscillations and mitochondrial associate membranes in non-excitable cells

Theoretical studies on calcium oscillations within the cytosolic [Ca$^{2+}$], and mitochondria [Ca$^{2+}$]$_{mit}$ have been conducted using a mathematical model-based approach. The model incorporates the mechanism of calcium-induced calcium release (CICR) through the activation of inositol-trisphosphate receptors (IPR), with a focus on the endoplasmic reticulum (ER) as an internal calcium store. The production of 1,4,5 inositol-trisphosphate (IP$_{3}$) through the phospholipase \(C\) isoforms and its degradation via Ca$^{2+}$ are considered, with IP$_{3}$ playing a crucial role in modulating calcium release from the ER. The model includes a simple kinetic mechanism for mitochondrial calcium uptake, release and physical connections between the ER and mitochondria, known as mitochondrial associate membrane complexes (MAMs), which influence cellular calcium homeostasis. Bifurcation analysis is used to explore the different dynamic properties of the model, identifying various regimes of oscillatory behavior and how these regimes change in response to different levels of stimulation, highlighting the complex regulatory mechanisms governing intracellular calcium signaling.

Dynamics of a Delayed Prey-Predator System With Crowley-Martin Functional Response

ln this paper, an ecological predator-prey model with time delay is proposed and studied analytically and numerically. The stability and bifurcation analysis of the proposed model with a Crowley-Martin response function are covered in this article. In the beginning, equilibrium points of the proposed model are identified. Secondly, the local asymptotic stability of the equilibria and Hopf-bifurcation are discussed by the characteristic equations of the system. Finally, by creating a suitable Lyapunov function and LaSalle’s invariance principle, the equilibria's global asymptotic stability is examined.

Activity patterns in ring networks of quadratic integrate-and-fire neurons with synaptic and gap junction coupling.

We consider a ring network of quadratic integrate-and-fire neurons with nonlocal synaptic and gap junction coupling. The corresponding neural field model supports solutions such as standing and traveling waves, and also lurching waves. We show that many of these solutions satisfy self-consistency equationswhich can be used to follow them as parameters are varied. We perform numerical bifurcation analysis of the neural field model, concentrating on the effects of varying gap junction coupling strength. Our methods are generally applicable to a wide variety of networks of quadratic integrate-and-fire neurons.

Predicting the firing behaviour of neural network through the bifurcation analysis of derivative mean-field model

The mean-field model is an important method for understanding the complex dynamics of the nervous system at different spatial levels and for simulating and theoretically analysing the collective dynamic behaviour of large neural populations. In the work, we construct an improved mean-field system of neural networks coupled with quadratic integrate-and-fire neurons and examine the discharge patterns for networks by analysing such a model, which is a three-dimensional smooth differential system. Bifurcation analysis of the mean-field model is conducted from both theoretical and simulation perspectives, we obtain the bifurcation conditions of some co-dimension-two bifurcations and divide the parameter space into different regimes by simulating two parameters bifurcation diagrams. We find a close correspondence, though with some variance, between the mean-field model and neural network when comparing the firing patterns of the two models in various parameter regimes. In summary, the obtained mean-field description builds the bridge between the parameters of neurons or networks and that of a mean-field system to ensure we can compare them and understand the connections between them. Specifically, the mean-field model can reflect the dynamics of neural networks from a macroscopic perspective, and its bifurcation can predict the behaviour of neural networks to a certain extent and understand the mechanisms behind them, such as bursting dynamics of neural networks.

Continuation of nonlinear normal modes using reduced-order models based on generalized characteristic value decomposition

AbstractOver the past two decades, data-driven reduced-order modeling (ROM) strategies have gained significant traction in the nonlinear dynamics community. Currently, several challenges in physical interpretation and data availability remain overlooked in current methodologies. This work proposes a novel ROM methodology based on a newly proposed generalized characteristic value decomposition (GCVD) to address these obstacles. The GCVD-ROM approach proposes a new perspective toward data-driven ROMs via characterization of the dynamics before any ROM considerations are made. In doing so, a significant degree of versatility is inherited in the GCVD-ROM strategy, allowing our models to reproduce the full-scale dynamics in different regions of the parameter space at the cost of a single training data set. Our approach utilizes computationally efficient free-decay data sets alongside a windowed-decomposition scheme, allowing us to extract energy-dependent modal structures for use in model-order reduction. This is accomplished using the physically insightful characteristic values provided by the GCVD, which are shown to be directly related to the system poles at a particular response amplitude. This natural metric, paired with a resonance tracking scheme, allows us to address the difficulties associated with physical interpretation and data availability without sacrificing the convenient aspects of linear projection-based model order reduction. A computational framework for the continuation and bifurcation analysis using linear projection-based ROMs is also presented, permitting us to deploy rigorous analysis and bifurcation studies to verify that our ROMs reproduce the intrinsic complexity of full-scale systems. A detailed walk-through of the GCVD-ROM approach is demonstrated on a simple system where important practical considerations and implementation details are discussed using a concrete example. The discretized von Kármán beam and shallow arch partial differential equations are also used to explore complicated scenarios involving modal coupling across disparate time scales and internal resonances.

Kudryashov-Sinelshchikov equation: phase portraits, bifurcation analysis and solitary waves

Kudryashov-Sinelshchikov equation: phase portraits, bifurcation analysis and solitary waves

Strong coupling yields abrupt synchronization transitions in coupled oscillators

Coupled oscillator networks often display transitions between qualitatively different phase-locked solutions—such as synchrony and rotating wave solutions—following perturbation or parameter variation. In the limit of weak coupling, these transitions can be understood in terms of commonly studied phase approximations. As the coupling strength increases, however, predicting the location and criticality of transition, whether continuous or discontinuous, from the phase dynamics may depend on the order of the phase approximation—or a phase description of the network dynamics that neglects amplitudes may become impossible altogether. Here we analyze synchronization transitions and their criticality systematically for varying coupling strength in theory and experiments with coupled electrochemical oscillators. First, we analyze bifurcations analysis of synchrony and splay states in an abstract phase model and discuss conditions under which synchronization transitions with different criticalities are possible. In particular, we show that such conditions can be understood by considering the relative contributions of higher harmonics to the phase dynamics. Second, we illustrate that transitions with different criticality indeed occur in experimental systems. Third, we highlight that the amplitude dynamics observed in the experiments can be captured in a numerical bifurcation analysis of delay-coupled oscillators. Our results showcase that reduced order phase models may miss important features that one would expect in the dynamics of the full system. Published by the American Physical Society 2024

Hopf bifurcation and stability analysis for a delayed equation with φ-Laplacian

A formal framework for the analysis of Hopf bifurcations for a kind of delayed equation with $\varphi$-Laplacian and with a discrete time delay is presented, thus generalizing known results for the sunflower equation given by Somolinos in 1978. Also, under appropriate assumptions we prove the gradient-like behavior of the equation which, in turn, implies the non-existence of nonconstant periodic solutions. Our conditions improve previous results known in the literature for the standard case $\varphi(x)=x$.

Bifurcation analysis and exploration of noise-induced transitions of a food chain model with Allee effect

Seeking shelter from threats is a widespread instinct across species, specially employed by prey to avoid direct confrontations with predators. The present investigation centers on a three-species food chain model wherein the basal prey, characterized by logistic growth, seeks refuge to evade the intermediate predator, while the intermediate predator, in turn, seeks refuge to avoid encounters with the top predator. Additionally, our model assumes that the presence of the top predator induces a mate-finding Allee effect among the intermediate predator. We investigate how varying levels of refuge, along with other critical parameters such as the reproduction rate of the basal prey and the natural mortality rate of the top predator, influence system’s dynamics within the biparametric planes. Our model displays multistability and undergoes transcritical, saddle–node, Bogdanov–Takens and cusp bifurcations across different parameters. Moreover, the external environmental noise can induce interesting dynamics in the predator–prey system, resulting in noise-induced frequent transitions between distinct interior attractors or from interior to axial attractors. This phenomenon is particularly notable in scenarios where the deterministic model exhibits tristability. In summary, our findings offer potential new avenues for developing control strategies within the realm of community ecology in constant as well as fluctuating environments.