arXiv subjects

Sanjay Pant

Publications and source records attributed to Sanjay Pant.

At least 19 recordsLinked to original sources

arXiv2025

Holographic Entanglement Negativity and Thermodynamics in Backreacted AdS Black Hole

We investigate holographic entanglement negativity (HEN) as a probe of mixed state quantum correlations in a deformed AdS black hole background with backreaction sourced by a string cloud. The bulk geometry is dual to a strongly coupled large-$N_c$ gauge theory at finite temperature, backreacted by a finite density of heavy static fundamental quarks. We analyze entanglement thermodynamics and establish a first law like relation in the small backreaction regime by identifying the deformed black hole at zero temperature as the natural ground state of the dual field theory. Using analytic expansions in the low and high temperature limits, we compute the HEN for adjacent, bipartite and disjoint subsystem configurations and examine its dependence on the backreaction parameter $\rho$. Our results reveal that at low temperature and high effective temperature, backreaction increases the HEN signaling enhancement of distillable quantum correlations. We further compare the behavior of HEN with holographic entanglement entropy (HEE) and mutual information (MI), demonstrating that entanglement negativity provides a sharper diagnostic of the competition between thermal and quantum correlations in holographic plasmas with matter backreaction.

hep-th↗

arXiv2025

Influence of Perfect Fluid Dark Matter on Shadow Observables of Yang-Mills modified charged black holes

We investigate the influence of perfect fluid dark matter (PFDM) on Yang--Mills--inspired charged black holes, with a particular focus on the resulting modifications to key black hole observables. By embedding a PFDM term into the spacetime geometry, we examine the alterations in shadow morphology, photon geodesics, and the associated energy emission spectra. Our analysis reveals that PFDM induces notable deviations in the shadow size, shape, and circularity, and significantly impacts the stability of circular orbits. Furthermore, the energy emission rate exhibits a strong dependence on both the Yang--Mills charge and the dark matter distribution. These results indicate that environmental effects arising from dark matter can imprint observable signatures on black hole shadows and radiation processes, offering a potential pathway to constrain dark matter models and probe non-Kerr geometries with forthcoming high-precision observations such as those from the Event Horizon Telescope and next-generation interferometers.

gr-qc↗

arXiv2025

Charged Black Hole with String Cloud Deformation: Entanglement and Chaos

We perform a holographic analysis of several quantum information theoretic observables entanglement entropy (EE), mutual information (MI), entanglement wedge cross section (EWCS), butterfly velocity ($v_B$) and thermo mutual information (TMI) in the background of charged AdS black hole deformed by a homogeneous string cloud. This configuration is dual to a large $\mathcal{N}_c$ strongly coupled field theory at finite temperature and finite chemical potential, in presence of quark cloud. We study how the entanglement structure and chaotic dynamics in the boundary theory are affected by the charge and backreaction. We find that both EE and EWCS increase monotonically with charge and backreaction, indicating enhanced correlations due to additional bulk degrees of freedom. On the other hand MI and TMI show a more intricate dependence backreaction tends to strengthen correlations, while increasing charge suppresses entanglement and makes the system more susceptible to scrambling. The analysis of the butterfly velocity indicates that both the presence of charge and the backreaction suppress the chaotic behavior of the system by reducing $v_B$. Furthermore, TMI exhibits a sharp transition under shockwave perturbations, with inter-boundary entanglement being entirely disrupted beyond a critical shock strength, which decreases with increasing charge.

hep-th↗

arXiv2025

Cardiovascular function changes following lung resection: a computational model to compare afterload increase and contractility loss mechanisms

Functional limitation after lung resection surgery has been consistently documented in clinical studies, and right ventricle (RV) dysfunction has been hypothesized as a contributing reason. However, the mechanisms of RV dysfunction after lung resection remain unclear, particularly whether change in afterload or contractility is the main cause. This study is the first to employ a lumped parameter model to simulate the effects of lung resection. The implementation of a computational model allowed us to isolate certain mechanisms that are difficult to perform clinically. Specifically, two mechanisms were compared: afterload increase and RV contractility loss. Furthermore, our rigorous approach included local and global sensitivity analyses to evaluate the effect of parameters on our results, both individually and collectively. Our results demonstrate that contractility and afterload exhibited consistent trends across various pressure and volume conditions, pulmonary artery systolic pressure, pulmonary artery diastolic pressure, and right ventricular systolic pressure showed opposite variations. The results show that post-operative RV dysfunction may result from a combination of RV contractility loss and afterload increase. Further exploration and refinement of this first computational model presented herein will help us predict RV dysfunction after lung resection and pave the way towards improving outcomes for lung cancer patients.

q-bio.TO↗

arXiv2024

Mixed state entanglement in deformed field theory at finite temperature

We study mixed state entanglement measures in a higher dimensional $T\bar{T}$ deformed field theory at finite temperature. The holographic dual is described by AdS$_{d+1}$ black brane geometry with a finite cutoff. We compute the entanglement wedge cross section (EWCS), proposed to be dual to entanglement of purification (EoP) and holographic entanglement negativity (HEN) for strip like subsystems. The behavior of EWCS and HEN is studied across different regimes of temperature and deformation parameter. It is observed that the deformation and temperature exhibit similar effects on these two entanglement measures. Increasing the deformation leads to a decrease in the entanglement between the subsystems.

hep-th↗

arXiv2023

Effect of backreaction on Island, Page curve and Mutual Information

We compute the entanglement entropy of Hawking radiation in a bath attached to a deformed eternal AdS black hole. This black hole is dual to the two identical strongly coupled large-$N_c$ thermal field theories, where each theory is backreacted (deformed) by the presence of a uniform static distribution of heavy fundamental quarks. In our observation we find that the entanglement entropy of Hawking radiation increases in a quadratic manner for an early time and linearly for the late time. The large time expression for the entanglement entropy of Hawking radiation is used to find the Page curve and Page time. After the Page time, the entanglement entropy saturates to a constant value due to the appearance of an island. We observe that introducing deformation (backreaction) delays the appearance of island and shifts the Page curve to a later time. Subsequently, the computation of the scrambling time reveals an increase with the backreaction parameter, suggesting a longer duration for information retrieval in the presence of deformation. Moreover, our analysis of the mutual information between the radiation subsystems shows that it vanishes at a critical time which increases with the deformation before the Page time. After the Page time, the appearance of island leads to the vanishing of mutual information between black hole subsystems and gives the time difference of the order of scrambling time.

hep-th↗

arXiv2023

Entanglement and chaos near critical point in strongly coupled gauge theory

We perform a holographic study of the high and low temperature behaviours of logarithmic negativity (LN) and entanglement wedge cross section (EWCS) in a large $N$ strongly coupled thermal field theory with critical point having a well defined gravity dual known as 1RC black hole. The critical point is defined via $\xi \to 2$ limit where, $\xi$ is dimensionless parameter proportional to the charge of the 1RC black hole. We show that the logarithmic negativity in low and high thermal limits enhances with increasing $\xi$. We analytically compute the EWCS in low and high thermal limits and find an agreement with the previously reported numerical results. We holographically explore the correlation between two identical copies of thermal field theory with critical point forming a thermofield double state (TFD) by computing the thermo mutual information (TMI). TMI shows an increasing behaviour with respect to the width of the boundary region. Further, we analyze the impact of an early perturbation on the field theory by analyzing a shock wave perturbation that grows exponentially in the dual eternal 1 RC black hole and then estimate the degradation of TMI. However rate of such disruption of TMI slows down as the value of critical parameter $\xi$ takes higher values.

hep-th↗

arXiv2022

Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues

Data-based approaches are promising alternatives to the traditional analytical constitutive models for solid mechanics. Herein, we propose a Gaussian process (GP) based constitutive modeling framework, specifically focusing on planar, hyperelastic and incompressible soft tissues. The strain energy density of soft tissues is modeled as a GP, which can be regressed to experimental stress-strain data obtained from biaxial experiments. Moreover, the GP model can be weakly constrained to be convex. A key advantage of a GP-based model is that, in addition to the mean value, it provides a probability density (i.e. associated uncertainty) for the strain energy density. To simulate the effect of this uncertainty, a non-intrusive stochastic finite element analysis (SFEA) framework is proposed. The proposed framework is verified against an artificial dataset based on the Gasser--Ogden--Holzapfel model and applied to a real experimental dataset of a porcine aortic valve leaflet tissue. Results show that the proposed framework can be trained with limited experimental data and fits the data better than several existing models. The SFEA framework provides a straightforward way of using the experimental data and quantifying the resulting uncertainty in simulation-based predictions.

cs.CE↗

arXiv2022

A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves

A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. 46 samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May--Newman model was the most probable model for the porcine aortic valve data. When the samples were grouped into different cusp types, the May--Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee--Sacks model and the May--Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode's amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to any tissue type, and has the potential to provide a structured and rational way of making simulations population-based.

q-bio.TO↗

arXiv2022

A holographic study of the characteristics of chaos and correlation in the presence of backreaction

In this work, we perform a holographic study to estimate the effect of backreaction on the correlation between two subsystems forming the thermofield double (TFD) state. Each of these subsystems is described as a strongly coupled large-$N_c$ thermal field theory, and the backreaction imparted to it is sourced by the presence of a uniform distribution of heavy static quarks. The TFD state we consider here holographically corresponds to an entangled state of two AdS blackholes, each of which is deformed by a uniform distribution of static strings. In order to make a holographic estimation of correlation between two entangled boundary field theories in presence of backreaction we compute the holographic mutual information in the backreacted eternal blackhole. The late time exponential growth of an early perturbation is a signature of chaos in the boundary thermal field theory. Using the shock wave analysis in the dual bulk theory, we characterize this chaotic behavior by computing the holographic butterfly velocity. We find that there is a reduction in the butterfly velocity due to a correction term that depends on the backreaction parameter. The late time exponential growth of an early perturbation destroys the two-sided correlation, whereas the backreaction always acts in favour of it. Finally we compute the entanglement velocity that essentially encodes the rate of disruption of correlation between two boundary theories.

hep-th↗

arXiv2021

Multiscale modelling of reversed Potts shunt as a potential palliative treatment for suprasystemic idiopathic pulmonary artery hypertension in children

Reversed Potts shunt (PS) was suggested as palliation for patients with suprasystemic pulmonary arterial hypertension (PAH) and right ventricular (RV) failure. PS, however, can result in poorly understood mortality. Here, a patient-specific geometrical multiscale model of PAH physiology and PS is developed for a paediatric PAH patient with stent-based PS. In the model, 7.6mm-diameter PS produces near-equalisation of the aortic and PA pressures and Qp/Qs (oxygenated vs deoxygenated blood flow) ratio of 0.72 associated with a 16% decrease of left ventricular (LV) output and 18% increase of RV output. The flow from LV to aortic arch branches increases by 16%, while LV contribution to the lower body flow decreases by 29%. Total flow in the descending aorta (DAo) increases by 18% due to RV contribution through the PS with flow into the distal PA branches decreasing. PS induces 18% increase of RV work due to its larger stroke volume pumped against lower afterload. Nonetheless, larger RV work does not lead to increased RV end-diastolic volume. Three-dimensional flow assessment demonstrates the PS jet impinging with a high velocity and wall shear stress on the opposite DAo wall with the most of the shunt flow being diverted to the DAo. Increasing the PS diameter from 5mm up to 10mm results in nearly linear decrease in post-operative Qp/Qs ratio. In conclusion, this model reasonably represents patient-specific haemodynamics pre- and post-creation of the PS, providing insights into physiology of this complex condition, and presents a predictive tool that could be useful for clinical decision-making regarding suitability for reversed PS in PAH patients with drug-resistant suprasystemic PAH.

physics.med-ph↗

arXiv2021

Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database

This study presents an application of machine learning (ML) methods for detecting the presence of stenoses and aneurysms in the human arterial system. Four major forms of arterial disease -- carotid artery stenosis (CAS), subclavian artery stenosis (SAC), peripheral arterial disease (PAD), and abdominal aortic aneurysms (AAA) -- are considered. The ML methods are trained and tested on a physiologically realistic virtual patient database (VPD) containing 28,868 healthy subjects, which is adapted from the authors previous work and augmented to include the four disease forms. Six ML methods -- Naive Bayes, Logistic Regression, Support Vector Machine, Multi-layer Perceptron, Random Forests, and Gradient Boosting -- are compared with respect to classification accuracies and it is found that the tree-based methods of Random Forest and Gradient Boosting outperform other approaches. The performance of ML methods is quantified through the F1 score and computation of sensitivities and specificities. When using all the six measurements, it is found that maximum F1 scores larger than 0.9 are achieved for CAS and PAD, larger than 0.85 for SAS, and larger than 0.98 for both low- and high-severity AAAs. Corresponding sensitivities and specificities are larger than 90% for CAS and PAD, larger than 85% for SAS, and larger than 98% for both low- and high-severity AAAs. When reducing the number of measurements, it is found that the performance is degraded by less than 5% when three measurements are used, and less than 10% when only two measurements are used for classification. For AAA, it is shown that F1 scores larger than 0.85 and corresponding sensitivities and specificities larger than 85% are achievable when using only a single measurement. The results are encouraging to pursue AAA monitoring and screening through wearable devices which can reliably measure pressure or flow-rates

cs.LG↗

arXiv2021

A physiologically realistic virtual patient database for the study of arterial haemodynamics

This study creates a physiologically realistic virtual patient database (VPD), representing the human arterial system, for the primary purpose of studying the affects of arterial disease on haemodynamics. A low dimensional representation of an anatomically detailed arterial network is outlined, and a physiologically realistic posterior distribution for its parameters is constructed through a Bayesian approach. This approach combines both physiological/geometrical constraints and the available measurements reported in the literature. A key contribution of this work is to present a framework for including all such available information for the creation of virtual patients (VPs). The Markov Chain Monte Carlo (MCMC) method is used to sample random VPs from this posterior distribution, and the pressure and flow-rate profiles associated with the VPs are computed through a model of pulse wave propagation. This combination of the arterial network parameters (representing the VPs) and the haemodynamics waveforms of pressure and flow-rates at various locations (representing functional response of the VPs) makes up the VPD. While 75,000 VPs are sampled from the posterior distribution, 10,000 are discarded as the initial burn-in period. A further 12,857 VPs are subsequently removed due to the presence of negative average flow-rate. Due to an undesirable behaviour observed in some VPs -- asymmetric under- and over-damped pressure and flow-rate profiles in the left and right sides of the arterial system -- a filter is proposed for their removal. The final VPD has 28,868 subjects. It is shown that the methodology is appropriate by comparing the VPD statistics to those reported in literature across real populations. A good agreement between the two is found while respecting physiological/geometrical constraints. The pre-filter database is made available at https://doi.org/10.5281/zenodo.4549764.

physics.med-ph↗

arXiv2021

A proof of concept study for machine learning application to stenosis detection

This proof of concept (PoC) assesses the ability of machine learning (ML) classifiers to predict the presence of a stenosis in a three vessel arterial system consisting of the abdominal aorta bifurcating into the two common iliacs. A virtual patient database (VPD) is created using one-dimensional pulse wave propagation model of haemodynamics. Four different machine learning (ML) methods are used to train and test a series of classifiers -- both binary and multiclass -- to distinguish between healthy and unhealthy virtual patients (VPs) using different combinations of pressure and flow-rate measurements. It is found that the ML classifiers achieve specificities larger than 80% and sensitivities ranging from 50-75%. The most balanced classifier also achieves an area under the receiver operative characteristic curve of 0.75, outperforming approximately 20 methods used in clinical practice, and thus placing the method as moderately accurate. Other important observations from this study are that: i) few measurements can provide similar classification accuracies compared to the case when more/all the measurements are used; ii) some measurements are more informative than others for classification; and iii) a modification of standard methods can result in detection of not only the presence of stenosis, but also the stenosed vessel.

cs.LG↗

arXiv2020

Effect of back reaction on entanglement and subregion volume complexity in strongly coupled plasma

The back reaction imparted by a uniform distribution of heavy static fundamental quarks on large $N_c$ strongly coupled gauge theory can be holographically realized as a deformation in $AdS$ blackhole background. The presence of back reaction brings significant changes in to the entanglement structure of the strongly coupled boundary theory at finite temperature. Since the deformed blackhole geometry still remains asymptotically $AdS$, the $gauge/gravity$ duality allows us to explore the entanglement structure of back reacted plasma in a quantitative way by computing various measures, e.g holographic entanglement entropy (HEE) and entanglement wedge cross section (EWCS). We explicitly study the variation of those entanglement measures with respect to the uniform density of heavy static fundamental quarks present in the boundary theory. In particular, we notice enhancement of both HEE and EWCS with respect to quark density. We also study the effect of back reaction on the holographic subregion volume complexity. In this analysis we observe an occurrence of logarithmic divergence proportional to the quark density parameter.

hep-th↗

arXiv2018

Beyond Newton: a new root-finding fixed-point iteration for nonlinear equations

Finding roots of equations is at the heart of most computational science. A well-known and widely used iterative algorithm is the Newton's method. However, its convergence depends heavily on the initial guess, with poor choices often leading to slow convergence or even divergence. In this paper, we present a new class of methods that improve upon the classical Newton's method. The key idea behind the new approach is to develop a relatively simple multiplicative transformation of the original equations, which leads to a significant reduction in nonlinearities, thereby alleviating the limitations of the Newton's method. Based on this idea, we propose two novel classes of methods and present their application to several mathematical functions (real, complex, and vector). Across all examples, our numerical experiments suggest that the new methods converge for a significantly wider range of initial guesses with minimal increase in computational cost. Given the ubiquity of Newton's method, an improvement in its applicability and convergence is a significant step forward, and will reduce computation times several-folds across many disciplines. Additionally, this multiplicative transformation may improve other techniques where a linear approximation is used.

math.NA↗

arXiv2018

On improving the numerical convergence of highly nonlinear elasticity problems

Finite elasticity problems commonly include material and geometric nonlinearities and are solved using various numerical methods. However, for highly nonlinear problems, achieving convergence is relatively difficult and requires small load step sizes. In this work, we present a new method to transform the discretized governing equations so that the transformed problem has significantly reduced nonlinearity and, therefore, Newton solvers exhibit improved convergence properties. We study exponential-type nonlinearity in soft tissues and geometric nonlinearity in compression, and propose novel formulations for the two problems. We test the new formulations in several numerical examples and show significant reduction in iterations required for convergence, especially at large load steps. Notably, the proposed formulation is capable of yielding convergent solution even when 10 to 100 times larger load steps are applied. The proposed framework is generic and can be applied to other types of nonlinearities as well.

math.NA↗

arXiv2017

Information sensitivity functions to assess parameter information gain and identifiability of dynamical systems

A new class of functions, called the `Information sensitivity functions' (ISFs), which quantify the information gain about the parameters through the measurements/observables of a dynamical system are presented. These functions can be easily computed through classical sensitivity functions alone and are based on Bayesian and information-theoretic approaches. While marginal information gain is quantified by decrease in differential entropy, correlations between arbitrary sets of parameters are assessed through mutual information. For individual parameters these information gains are also presented as marginal posterior variances, and, to assess the effect of correlations, as conditional variances when other parameters are given. The easy to interpret ISFs can be used to a) identify time-intervals or regions in dynamical system behaviour where information about the parameters is concentrated; b) assess the effect of measurement noise on the information gain for the parameters; c) assess whether sufficient information in an experimental protocol (input, measurements, and their frequency) is available to identify the parameters; d) assess correlation in the posterior distribution of the parameters to identify the sets of parameters that are likely to be indistinguishable; and e) assess identifiability problems for particular sets of parameters.

stat.ME↗