arXiv subjects

N. Nirmal Thyagu

Publications and source records attributed to N. Nirmal Thyagu.

11 recordsLinked to original sources

arXiv2026

Topological Feature Extraction of Scanty Time Series Data: A Data-Driven Approach for Dynamic State Change Detection

Complex dynamical systems often undergo transitions from periodic to chaotic behaviour as bifurcation parameters vary, making timely detection of these changes essential. Conventional approaches based on the maximal Lyapunov exponent (MLE) generally require either knowledge of the governing equations or sufficiently long, uniformly sampled time series. Their performance degrades when the available data are scanty or contain missing observations, making reliable phase-space reconstruction difficult. We propose a methodology that combines Topological Data Analysis (TDA), specifically 0-D sublevel persistence, with Machine Learning (ML) classifiers to distinguish periodic and chaotic regimes directly from time series. Sublevel persistence extracts topological features by analysing the evolution of minima and maxima, revealing repeating signatures for periodic dynamics and more scattered patterns for chaotic dynamics. These features are used to train logistic regression, support vector machine, and k-nearest neighbour classifiers. Hyperparameters are validated using K-fold cross-validation, yielding average classification accuracies exceeding 90%. The trained classifiers provide binary predictions, identifying periodic and chaotic behaviour in previously unseen data. The proposed methodology is evaluated on the Duffing, Rössler, and Lorenz systems, where the detected transitions closely agree with those identified using the MLE, demonstrating the reliability of the approach. It is further applied to real-world ECG signals to classify normal and abnormal heartbeats, producing encouraging performance across standard statistical metrics. The proposed framework provides an effective alternative for analysing sparse or incomplete time series and is particularly useful in experimental settings where conventional nonlinear time-series methods are limited.

nlin.CD↗

arXiv2024

Inertial Particle Dynamics in Traveling Wave Flow

The dynamics of inertial particles in fluid flows have been the focus of extensive research due to their relevance in a wide range of industrial and environmental processes. Earlier studies have examined the dynamics of aerosols and bubbles using the Maxey-Riley equation in some standard systems but their dynamics within the traveling wave flow remain unexplored. In this paper, we study the Lagrangian dynamics of inertial particles in the traveling wave flow which shows mixing, and segregation in phase space as well as the formation of Lagrangian Coherent Structures (LCS). We first obtain the finite-time Lyapunov exponent (FTLEs) for the base fluid flow defined by the traveling wave flow using the Cauchy-Green deformation tensor. Further, we extend our calculations to the inertial particles to get the inertial finite-time Lyapunov exponent (iFTLEs). Our findings reveal that heavier inertial particles tend to be attracted to the ridges of the FTLE fields, while lighter particles are repelled. By understanding how material elements in a flow separate and stretch, one can predict pollutant dispersion, optimize the mixing process, and improve navigation and tracking in fluid environments. This provides insights into the complex and non-intuitive behavior of inertial particles in chaotic fluid flows, and may have implications for pollutant transport in wide-ranging fields such as atmospheric and oceanic sciences.

physics.flu-dyn↗

arXiv2024

Characterization of dynamical systems with scanty data using Persistent Homology and Machine Learning

Determination of the nature of the dynamical state of a system as a function of its parameters is an important problem in the study of dynamical systems. This problem becomes harder in experimental systems where the obtained data is inadequate (low-res) or has missing values. Recent developments in the field of topological data analysis have given a powerful methodology, viz. persistent homology, that is particularly suited for the study of dynamical systems. Earlier studies have mapped the dynamical features with the topological features of some systems. However, these mappings between the dynamical features and the topological features are notional and inadequate for accurate classification on two counts. First, the methodologies employed by the earlier studies heavily relied on human validation and intervention. Second, this mapping done on the chaotic dynamical regime makes little sense because essentially the topological summaries in this regime are too noisy to extract meaningful features from it. In this paper, we employ Machine Learning (ML) assisted methodology to minimize the human intervention and validation of extracting the topological summaries from the dynamical states of systems. Further, we employ a metric that counts in the noisy topological summaries, which are normally discarded, to characterize the state of the dynamical system as periodic or chaotic. This is surprisingly different from the conventional methodologies wherein only the persisting (long-lived) topological features are taken into consideration while the noisy (short-lived) topological features are neglected. We have demonstrated our ML-assisted method on well-known systems such as the Lorentz, Duffing, and Jerk systems. And we expect that our methodology will be of utility in characterizing other dynamical systems including experimental systems that are constrained with limited data.

nlin.CD↗

arXiv2018

Characterizing the complexity of time series network graphs: A simplicial approach

We analyze the time series obtained from different dynamical regimes of the logistic map by constructing their equivalent time series (TS) networks, using the visibility algorithm. The regimes analyzed include both periodic and chaotic regimes, as well as intermittent regimes and the Feigenbaum attractor at the edge of chaos. We use the methods of algebraic topology to define the simplicial characterizers, which can analyse the simplicial structure of the networks at both the global and local levels. The simplicial characterisers bring out the hierarchical levels of complexity at various topological levels. These hierarchical levels of complexity find the skeleton of the local dynamics embedded in the network which influence the global dynamical properties of the system, and also permit the identification of dominant motifs. We also analyze the same networks using conventional network characterizers such as average path lengths and clustering coefficients. We see that the simplicial characterizers are capable of distinguishing between different dynamical regimes and can pick up subtle differences in dynamical behavior, whereas the usual characterizers provide a coarser characterization. However, the two taken in conjunction, can provide information about the dynamical behavior of the time series, as well as the correlations in the evolving system. Our methods can therefore provide powerful tools for the analysis of dynamical systems.

physics.soc-ph↗

arXiv2018

Local analysis of the history dependence in tetrahedra packings

The mechanical properties of a granular sample depend frequently on the way the packing was prepared. However, is not well understood which properties of the packing store this information. Here we present an X-ray tomography study of three pairs of tetrahedra packings prepared with three different tapping protocols. The packings in each pair differs in the number of mechanical constraints $C$ imposed on the particles by their contacts, while their bulk volume fraction $ϕ_{global}$ is approximately the same. We decompose $C$ into the contributions of the three different contact types possible between tetrahedra -- face-to-face (F2F), edge-to-face (E2F), and point contacts -- which each fix a different amount of constraints. We then perform a local analysis of the contact distribution by grouping the particles together according to their individual volume fraction $ϕ_{local}$ computed from a Voronoi tessellation. We find that in samples which have been tapped sufficiently long the number of F2F contacts becomes an universal function of $ϕ_{local}$. In contrast the number of E2F and point contacts varies with the applied tapping protocol. Moreover, we find that the anisotropy of the shape of the Voronoi cells depends on the tapping protocol. This behavior differs from spheres and ellipsoids and posses a significant constraint for any mean-field approach to tetrahedra packings.

cond-mat.soft↗

arXiv2017

The Simplicial Characterisation of TS networks: Theory and applications

We use the visibility algorithm to construct the time series networks obtained from the time series of different dynamical regimes of the logistic map. We define the simplicial characterisers of networks which can analyse the simplicial structure at both the global and local levels. These characterisers are used to analyse the TS networks obtained in different dynamical regimes of the logisitic map. It is seen that the simplicial characterisers are able to distinguish between distinct dynamical regimes. We also apply the simplicial characterisers to time series networks constructed from fMRI data, where the preliminary results indicate that the characterisers are able to differentiate between distinct TS networks.

stat.ME↗

arXiv2010

Competitive cluster growth on networks: complex dynamics and survival strategies

We extend the study of a model of competitive cluster growth in an active medium to a basis of networks; this is done by adding nonlocal connections with probability $p$ to sites on a regular lattice, thus enabling one to interpolate between regularity and full randomness. The model on networks demonstrates high sensitivity to small changes in initial configurations, which we characterize using damage spreading. The main focus of this paper is, however, the devising of survival strategies through selective networking, to alter the the fate of an arbitrarily chosen cluster: whether this be to revive a dying cluster to life, or to make a weak survivor into a stronger one. Although such goals are typically achieved by networking with relatively small clusters, our results suggest that it ought to be possible also to network with peers and larger clusters. The main indication of this comes from the probability distributions of mass differences between survivors and their immediate neighbours, which show an astonishing universality; they suggest strategies for winning against the odds.

cond-mat.stat-mech↗

arXiv2009

Transport and diffusion in the embedding map

We study the transport properties of passive inertial particles in a $2-d$ incompressible flows. Here the particle dynamics is represented by the $4-d$ dissipative embedding map of $2-d$ area-preserving standard map which models the incompressible flow. The system is a model for impurity dynamics in a fluid and is characterized by two parameters, the inertia parameter $α$, and the dissipation parameter $γ$. We obtain the statistical characterisers of transport for this system in these dynamical regimes. These are, the recurrence time statistics, the diffusion constant, and the distribution of jump lengths. The recurrence time distribution shows a power law tail in the dynamical regimes where there is preferential concentration of particles in sticky regions of the phase space, and an exponential decay in mixing regimes. The diffusion constant shows behaviour of three types - normal, subdiffusive and superdiffusive, depending on the parameter regimes. Phase diagrams of the system are constructed to differentiate different types of diffusion behaviour, as well as the behaviour of the absolute drift. We correlate the dynamical regimes seen for the system at different parameter values with the transport properties observed at these regimes, and in the behaviour of the transients. This system also shows the existence of a crisis and unstable dimension variability at certain parameter values. The signature of the unstable dimension variability is seen in the statistical characterisers of transport. We discuss the implications of our results for realistic systems.

nlin.CD↗

arXiv2008

Crisis and unstable dimension variability in the bailout embedding map

The dynamics of inertial particles in $2-d$ incompressible flows can be modeled by $4-d$ bailout embedding maps. The density of the inertial particles, relative to the density of the fluid, is a crucial parameter which controls the dynamical behaviour of the particles. We study here the dynamical behaviour of aerosols, i.e. particles heavier than the flow. An attractor widening and merging crisis is seen the phase space in the aerosol case. Crisis induced intermittency is seen in the time series and the laminar length distribution of times before bursts gives rise to a power law with the exponent $β=-1/3$. The maximum Lyapunov exponent near the crisis fluctuates around zero indicating unstable dimension variability (UDV) in the system. The presence of unstable dimension variability is confirmed by the behaviour of the probability distributions of the finite time Lyapunov exponents.

nlin.CD↗

arXiv2008

Chaotic advection of inertial particles in two dimensional flows

We study the dynamics of inertial particles in two dimensional incompressible flows. The Maxey-Riley equation describing the motion of inertial particles is used to construct a four dimensional dissipative bailout embedding map. This map models the dynamics of the inertial particles while the base flow is represented by a 2-d area preserving map. The dynamics of particles heavier than the fluid, the aerosols, as well as that of bubbles, particles lighter than the fluid, can be classified into 3 main dynamical regimes - periodic orbits, chaotic structures and mixed regions. A phase diagram in the parameter space is constructed with the Lyapunov characteristic exponents of the 4-d map in which these dynamical regimes are distinctly identified. The embedding map can target periodic orbits, as well as chaotic structures, in both the aerosol and bubble regimes, at suitable values of the dissipation parameter.

nlin.CD↗

arXiv2007

Clustering, Chaos and Crisis in a Bailout Embedding Map

We study the dynamics of inertial particles in two dimensional incompressible flows. The particle dynamics is modelled by four dimensional dissipative bailout embedding maps of the base flow which is represented by 2-d area preserving maps. The phase diagram of the embedded map is rich and interesting both in the aerosol regime, where the density of the particle is larger than that of the base flow, as well as the bubble regime, where the particle density is less than that of the base flow. The embedding map shows three types of dynamic behaviour, periodic orbits, chaotic structures and mixed regions. Thus, the embedding map can target periodic orbits as well as chaotic structures in both the aerosol and bubble regimes at certain values of the dissipation parameter. The bifurcation diagram of the 4-d map is useful for the identification of regimes where such structures can be found. An attractor merging and widening crisis is seen for a special region for the aerosols. At the crisis, two period-10 attractors merge and widen simultaneously into a single chaotic attractor. Crisis induced intermittency is seen at some points in the phase diagram. The characteristic times before bursts at the crisis show power law behaviour as functions of the dissipation parameter. Although the bifurcation diagram for the bubbles looks similar to that of aerosols, no such crisis regime is seen for the bubbles. Our results can have implications for the dynamics of impurities in diverse application contexts.

nlin.CD↗