arXiv subjects

Sanjeev Kumar

Publications and source records attributed to Sanjeev Kumar.

At least 37 records · Page 2Linked to original sources

arXiv2025

Role of varying Reynolds number for flow past a rotating cylinder at high rotation rate

The present study reports comprehensive bifurcation analysis of flow past a rotating cylinder at a fixed rotation rate by varying free-stream Reynolds number ($Re_{\infty}$) from 1000-6000 in intervals of 50. Two-dimensional compressible Navier-Stokes equations are solved using dispersion relation preserving numerical methods over 101 test cases, amounting to $10^6$ core hours of computing. The dataset produced from high-fidelity simulations serve as useful benchmarking tools for testing compressible flow solvers, estimating unsteady force distribution and vorticity dynamics. For moderate $Re_{\infty}$, rotation induces circulation that reduces pressure drag with increasing $Re_{\infty}$. For higher $Re_{\infty}$, boundary layer becomes thinner with suppressed flow separation, but effect of rotation saturates. Thus, benefits of increasing $Re_{\infty}$ taper off and pressure recovery stalls. The bifurcation analysis reveals a critical $Re_{\infty}$ of 5650 beyond which global behavior of Magnus-Robins effect changes significantly. Supercritical flow is receptive to time-dependent instabilities and structures in wake of the cylinder become dynamically unstable. Even small changes in $Re_{\infty}$ leads to different instantaneous force distributions and sharp fluctuations in lift and drag calculations. Stronger, coherent vortices in the wake generate consistent, high-energy periodic signals, contributing to strong Fourier amplitudes in spectra. An artificial neural network (ANN) is trained using simulation datasets to serve as fast, inexpensive alternatives for calculating lift, drag, and onset time of instability. The ANN reduces time required for simulation by 99.9\%, enabling dense parametric sweeps. Maximum accuracy achieved for the ANN is between 90-99\% for the parameters examined.

physics.flu-dyn↗

arXiv2025

A Quantum Bagging Algorithm with Unsupervised Base Learners for Label Corrupted Datasets

The development of noise-resilient quantum machine learning (QML) algorithms is critical in the noisy intermediate-scale quantum (NISQ) era. In this work, we propose a quantum bagging framework that uses QMeans clustering as the base learner to reduce prediction variance and enhance robustness to label noise. Unlike bagging frameworks built on supervised learners, our method leverages the unsupervised nature of QMeans, combined with quantum bootstrapping via QRAM-based sampling and bagging aggregation through majority voting. Through extensive simulations on both noisy classification and regression tasks, we demonstrate that the proposed quantum bagging algorithm performs comparably to its classical counterpart using KMeans while exhibiting greater resilience to label corruption than supervised bagging methods. This highlights the potential of unsupervised quantum bagging in learning from unreliable data.

quant-ph↗

arXiv2025

Vacancy induced expansion of spin-liquid regime in J1-J2 Heisenberg model

We study the model for spin-1/2 J1-J2 Heisenberg antiferromagnets on a square lattice in the presence of spin vacancies. In order to overcome the methodological challenges associated with analyzing models with magnetic frustration and inhomogeneities, we introduce a new semi-classical approach in which singlet dimers are treated as effective classical degrees of freedom. The energetic and entropic aspects of the dimer formation are included via a classical Monte Carlo scheme that allows for the dynamical conversion of spin pairs into dimers and vice versa. We show that our semi-classical approach recovers the qualitative physics of the J1-J2 model in the absence of vacancies. The vacancies lead to a broadening of the spin-liquid regime between the Néel and the stripe antiferromagnetic phases. This suggests a possible new route to discover spin-liquid ground states by tuning the J2/J1 ratio in doped square lattice antiferromagnets.

cond-mat.str-el↗

arXiv2025

ADALog: Adaptive Unsupervised Anomaly detection in Logs with Self-attention Masked Language Model

Modern software systems generate extensive heterogeneous log data with dynamic formats, fragmented event sequences, and varying temporal patterns, making anomaly detection both crucial and challenging. To address these complexities, we propose ADALog, an adaptive, unsupervised anomaly detection framework designed for practical applicability across diverse real-world environments. Unlike traditional methods reliant on log parsing, strict sequence dependencies, or labeled data, ADALog operates on individual unstructured logs, extracts intra-log contextual relationships, and performs adaptive thresholding on normal data. The proposed approach utilizes a transformer-based, pretrained bidirectional encoder with a masked language modeling task, fine-tuned on normal logs to capture domain-specific syntactic and semantic patterns essential for accurate anomaly detection. Anomalies are identified via token-level reconstruction probabilities, aggregated into log-level scores, with adaptive percentile-based thresholding calibrated only on normal data. This allows the model to dynamically adapt to evolving system behaviors while avoiding rigid, heuristic-based thresholds common in traditional systems. We evaluate ADALog on benchmark datasets BGL, Thunderbird, and Spirit, showing strong generalization and competitive performance compared to state-of-the-art supervised and unsupervised methods. Additional ablation studies examine the effects of masking, fine-tuning, and token positioning on model behavior and interpretability.

cs.AI↗

arXiv2025

Precise charge state distribution of projectile ions through solid targets

The charge state distribution (CSD) of the projectile ions through solid targets in the intermediate energy range (1 MeV/u $<$ E $<$ 4 MeV/u) has a major impact on the collision of the ion atom and accelerator physics. We explore the mean charge states taken from the empirical formula [Schiwietz $et~al.$, Nucl. Inst. Meths. {\bf 225}, 4(2004)] are only good for projectile ions with $Z_1 \le 16$. To solve this issue, we develop a model in which instead of a single formula, if we employ four formulae, the comparative picture between experimental and empirical data becomes impressive. Furthermore, the CSDs with the mean charge state so obtained and the Gaussian distribution function having distribution width given by [Novikov and Teplove, Phys. Lett. {\bf378}, 1286(2014)] compare well with the experimentally measured CSDs for the entire range of projectile ions. We believe that precise CSDs will be highly useful in both ion-atom collision and accelerator physics.

physics.atom-ph↗

arXiv2025

A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM

Accurate identification of material parameters is crucial for predictive modeling in computational mechanics. The two primary approaches in the experimental mechanics' community for calibration from full-field digital image correlation data are known as finite element model updating (FEMU) and the virtual fields method (VFM). In VFM, the objective function is a squared mismatch between internal and external virtual work or power. In FEMU, the objective function quantifies the weighted mismatch between model predictions and corresponding experimentally measured quantities of interest. It is minimized by iteratively updating the parameters of an FE model. While FEMU is seen as more flexible, VFM is commonly used instead of FEMU due to its considerably greater computational expense. However, comparisons between the two methods usually involve approximations of gradients or sensitivities with finite difference schemes, thereby making direct assessments difficult. Hence, in this study, we rigorously compare VFM and FEMU in the context of numerically-exact sensitivities obtained through local sensitivity analyses and the application of automatic differentiation software. To this end, both methods are tested on a finite strain elastoplasticity model. We conduct a series of test cases to assess both methods' robustness under practical challenges.

cs.CE↗

arXiv2025

Enhanced Crystallization and Evaporation Retardation in Mixed Surfactant Systems at the Air-Water Interface: A Study on Chain Length Compatibility and Molecular Ratio

Effects of chain length compatibility and molecular ratio on the two-dimensional crystallization of a binary mixed surfactant system with non-identical molecular size and its consequence on retardation to water evaporation are described via Langmuir Blodgett films. The mixed monolayers corresponding to 1:3 exhibit minimal area per molecule owing to identical chain length. The maximum crystallization was also observed at this ratio from BAM images at constant surface pressure. The prominent changes in the physical properties of the analyzed system, for the 1:3 molecular ratio, are attributed to the augmented stability mediated by the hexagonal closed packing and packing behavior in the mixed monolayer. The observation was validated from a random ball mixing model and simulation study. The maximum retardation to evaporation was also observed for the 1:3 molecular ratio and is attributed to augmented stability and spreading of the monolayers at the air-water interface.

cond-mat.soft↗

arXiv2025

Deep Learning for Early Alzheimer Disease Detection with MRI Scans

Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset. Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data. We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency. This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.

cs.CV↗

arXiv2024

Site Selective Spontaneous Symmetry Breaking and Partial Order in Kondo Lattices

Using the combination of a new effective Hamiltonian approach and hybrid Monte-Carlo simulations, we unveil a variety of partially magnetically ordered (PMO) phases in the Kondo lattice model. Our approximation is motivated by two crucial features of the Hamiltonian: (i) formation of Kondo singlets leading to vanishing local magnetic moments, and (ii) spatially correlated nature of the effective single-particle kinetic energy. We discover PMO phases with fractional values $1/4$, $3/8$, and $1/2$ of Kondo-screened sites. A common understanding of these states emerges in terms of a non-local ordering mechanism. The concept of site-selective spontaneous symmetry breaking introduced here provides a new general approach to study models of interacting fermions in the intermediate coupling regime.

cond-mat.str-el↗

arXiv2024

Myriad of Terahertz Magnons with All-Optical Magnetoelectric Functionality for Efficient Spin-Wave Computing in Honeycomb Magnet Co4Ta2O9

Terahertz (THz) magnonics represent the notion of mathematical algebraic operations of magnons such as addition and subtraction in THz regime which is an emergent dissipationless ultrafast alternative to existing data processing technologies. Spin waves on antiferromagnets with a twist in spin order host such magnons in THz regime, which possess advantage of higher processing speeds, additional polarization degree of freedom and longer propagation lengths compared to that of gigahertz magnons in ferromagnets. While interaction among THz magnons is the crux of algebra operations, it requires magnetic orders with closely spaced magnon modes for easier experimental realization of their interactions. Herein, rich wealth of magnons spanning a narrow energy range of 0.4 to 10 meV is unraveled in Co4Ta2O9 using magneto-THz spectroscopy. Rare multitude of ten excitation modes, either of magnons or hybrid magnon-phonon modes is presented. Among other attributes, spin lattice interaction suggests a correlation among spin and local lattice distortion, magnetostriction, and magnetic exchange interaction signifying a THz magnetoelectric effect. This unification of structural, magnetic and dielectric facets, and their magnetic field control in a narrow spectrum unwinds the mechanism underneath the system's complexity while the manifestation of multitude of spin excitation modes is a potential source to design multiple channels in spin-wave computing based devices.

cond-mat.mtrl-sci↗

arXiv2024

Terahertz crystal-field transitions and quasi ferromagnetic magnon excitations in a noncollinear magnet for hybrid spin-wave computation

The complexity of interactions between the crystal-field and unusual non-collinear spin arrangement in non-trivial magnets demands novel tools to unravel the mystery underneath. In this work, we study such interaction dynamics of crystal-field-excitations (CFE) and low-energy magnetic excitations in orthochromite TmCrO3 with controls of temperature and magnetic field using high-resolution magneto-terahertz (THz) time-domain spectroscopy. The THz energy spectrum spanning 0.5-10 meV possesses a low-frequency spin-excitation (magnon) mode and a multitude of CFE modes at 10 K, all of which uniquely embody a range of phenomena. For the magnon mode, a temperature dependence of peak frequency is induced by magnetic interactions between Tm and Cr subsystems. While a change from blue- to red-shift of peak frequency of this mode marks the magnetization reversal transition, the spin reorientation temperature and change of magnetic anisotropy are depicted by different features of field- and temperature-dependent peak frequency dynamics. The modes corresponding to CFE are robust and laden with a multitude of sub-modes which are attributes of non-trivial interactions across different transitions. These modes are suppressed only upon substitution of Tb3+ at Tm3+ site, which suggests a dominant role of single-ion anisotropy in controlling entire THz excitations spectra. Overall, this remarkable range of phenomena seen through the unique lens of all-optical THz tools provides deeper insights into the origin of magnetic phases in systems with complex interactions between rare-earth and transition metal ions and provides a multitude of a novel combination of closely spaced modes for emerging hybrid spin-wave computation.

cond-mat.mtrl-sci↗

arXiv2024

On Lepton Flavor Violation and Dark Matter in Scotogenic model with Trimaximal Mixing

We examine the Scotogenic model employing the TM$_2$ mixing matrix, $U_{\text{TM}_2}$, for neutrinos and parameterize the Yukawa coupling matrix $y$ based on the diagonalization condition for the neutrino mass matrix, $m_ν$. Our investigation centers on analyzing the relic density of cold dark matter ($Ωh^2$) and possible lepton flavor violation (LFV) in the model. In particular, we study coannihilation dynamics and LFV, in the model, considering various coannihilation scenarios including non-zero mass splitting between lightest sterile neutrinos. While analyzing, we have taken into consideration respective experimental constraints on $Ωh^2$ and LFV alongside neutrino oscillation data. Our study reveals that in both normal and inverted hierarchy of neutrino masses, splitting between masses of $N_1$ and $N_2$ can be up to $\approx 15\%$ for the model to be in consonance with the above constraints. In the second part, we have extended the analysis incorporating extended magic symmetry in $m_ν$ enabling us to completely determine Yukawa coupling matrix ($y$). We observe a notable exclusion of the effective Majorana mass $|m_{ee}|$ parameter space by cosmological bound on sum of neutrino masses, particularly in the normal hierarchy while inverted hierarchy scenario is excluded due to constraints coming from extended magic symmetry. These findings shed light on the interplay among the Scotogenic model, TM$_2$ mixing, and extended magic symmetry, offering insights into the permitted parameter space and hierarchy exclusion.

hep-ph↗

arXiv2024

Neutrino Mass Matrices with Generalized CP Symmetries and Texture Zeros

We investigate the properties of neutrino mass matrices that incorporate texture zeros and generalized CP symmetries associated with tribimaximal mixing. By combining these approaches, we derive predictive neutrino mass matrices and explore their implications for mass hierarchies, mixing angles, and CP-violating phases. We find that the three angles defining the generalized CP symmetries have narrow allowed ranges. We also obtain distinct correlations between the three mixing angles and the CP-violating phases that distinguish the various texture patterns from one another. Moreover, we compute the effective neutrino mass for neutrinoless double beta decay and the sum of neutrino masses. Our results highlight the predictability and testability of neutrino mass matrices with generalized CP symmetry.

hep-ph↗

arXiv2024

Robustness of a state with Ising topological order against local projective measurements

We investigate the fragility of a topologically ordered state, namely, the ground state of a weakly Zeeman perturbed honeycomb Kitaev model to environment induced decoherence effects mimicked by random local projective measurements. Our findings show the nonabelian Ising topological order, as quantified by a tripartite mutual information (the topological entanglement entropy $γ$,) is resilient to such disturbances. Further, $γ$ is found to evolve smoothly from a topologically ordered state to a distribution of trivial states as a function of rate of measurement (temperature). We assess our model by contrasting it with the Toric Code limit of the Kitaev model, whose ground state has abelian $Z_2$ topological order, and which has garnered greater attention in the literature of fault-tolerant quantum computation. The findings reveal the topological order in the Toric Code limit collapses rapidly as opposed to our model where it can withstand higher measurement rates.

cond-mat.str-el↗

arXiv2024

A space-time gauge theory for modeling ductile damage and its NOSB peridynamic implementation

Local translational and scaling symmetries in space-time is exploited for modelling ductile damage in metals and alloys over wide ranges of strain rate and temperature. The invariant energy density corresponding to the ductile deformation is constructed through the gauge invariant curvature tensor by imposing the Weyl like condition. The energetics of the plastic deformation is brought in through the gauge compensating field emerged due to local translation. Invariance of the energy density under the local action of translation and scaling is preserved through minimally replaced space-time gauge covariant operators. Minimal replacement introduces two non-trivial gauge compensating fields pertaining to local translation and scaling. These are used to describe ductile damage, including plastic flow and micro-crack evolution in the material. A space-time pseudo-Riemannian metric is used to lay out the kinematics in a finite-deformation setting. Recognizing the available insights in classical theories of viscoplasticity, we also establish a correspondence of the gauge compensating field due to spatial translation with Kröner's multiplicative decomposition of the deformation gradient. Thermodynamically consistent coupling between viscoplasticity and ductile damage is ensured through an appropriate degradation function. Non-ordinary state-based (NOSB) peridynamics (PD) discretization of the model is used for numerical implementation. The model's viability is tested in reproducing a few experimentally known facts, viz., strain rate locking in the stress-strain response, whose origin is traced to a nonlinear microscopic inertia term arising out of the space-time translation symmetry. Finally, we solved 2D and axisymmetric deformation problems for qualitatively validating the model's viability. NOSB peridynamics axisymmetric formulation in finite deformation setup is also presented.

cond-mat.mtrl-sci↗

arXiv2023

Irreducibility via location of zeros

In this paper, we obtain several new classes of irreducible polynomials having integer coefficients whose zeros lie inside an open disk around the origin or outside a closed annular region in the complex plane. Such irreducible polynomials are devised by imposing Perron--type sufficiency conditions on their coefficients.

math.NT↗

arXiv2023

Quantum multi-secret sharing scheme with access structures and cheat identification

This work proposes a $d$-dimensional quantum multi-secret sharing scheme with a cheat detection mechanism. The dealer creates multiple secrets and distributes the shares of these secrets using multi-access structures and a monotone span program. The dealer detects the cheating of each participant using the Black box's cheat detection mechanism. To detect the participants' deceit, the dealer distributes secret shares' shadows derived from a randomly invertible matrix $X$ to the participants, stored in the black box. The Black box identifies the participant's deceitful behavior during the secret recovery phase. Only honest participants authenticated by the Black box acquire their secret shares to recover the multiple secrets. After the Black box cheating verification, the participants reconstruct the secrets by utilizing the unitary operations and quantum Fourier transform. The proposed protocol is reliable in preventing attacks from eavesdroppers and participants. The scheme's efficiency is demonstrated in different noise environments: dit-flip noise, $d$-phase-flip noise, and amplitude-damping noise, indicating its robustness in practical scenarios. The proposed protocol provides greater versatility, security, and practicality.

quant-ph↗

arXiv2023

Multi-physics modeling of non-equilibrium phenomena in inductively coupled plasma discharges: Part I. A state-to-state approach

This work presents a vibrational and electronic state-to-state model for nitrogen plasma implemented within a multi-physics modular computational framework to study non-equilibrium effects in inductively coupled plasma (ICP) discharges. Within the computational framework, the set of vibronic (i.e., vibrational and electronic) master equations are solved in a tightly coupled fashion with the flow governing equations. This tight coupling eliminates the need for invoking any simplifying assumptions when computing the state of the plasma, thereby ensuring a higher degree of physical fidelity. To mitigate computational complexity, a maximum entropy coarse-graining strategy is deployed, effectively truncating the internal state space. The efficacy of this reduced StS model is empirically substantiated through zero-dimensional isochoric simulations. In these simulations, the results obtained from the reduced-order model are rigorously compared against those obtained from the full StS model, thereby confirming the accuracy of the reduced StS framework. The developed Coarse-grained StS model was employed to study the plasma discharge within the VKI Plasmatron facility. Our results reveal pronounced discrepancies between the plasma flow fields obtained from StS simulations and those derived from Local Thermodynamic Equilibrium (LTE) models, which are conventionally used in the simulation of such facilities. The analysis demonstrates a substantial departure of the internal state populations of atoms and molecules from the Boltzmann distribution. These nonequilibrium effects have important consequences on the energy coupling dynamics, thereby impacting the overall morphology of the plasma discharge. A deeper analysis of the results demonstrates that the population distribution is in a Quasi-Steady-State in the hot plasma core.

physics.plasm-ph↗