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Dinesh Kumar

Publications and source records attributed to Dinesh Kumar.

At least 37 records · Page 2Linked to original sources
arXiv2025

Crystal Growth & Physical Property Characterization of Mixed Topological Insulator BiSbTe$_3$

This article reports the synthesis of a single crystalline mixed topological insulator (TI) BiSbTe$_3$ and its detailed structural and magneto-transport properties. The single crystalline samples of BiSbTe$_3$ are grown by the melt-growth process and characterized by X-ray diffraction (XRD), Energy dispersive X-ray analysis (EDAX) and Raman spectroscopy. The single crystal XRD peaks dictated the growth direction along the c-axis. The Raman spectrum elucidated the characteristic peaks of the mixed topological insulator. The broadening of Raman peaks exhibited the formation of Te-Bi-Te and Te-Sb-Te bonds and associated vibrational modes. The single crystals are characterized by magneto-transport measurements down to 2 K and up to 14 Tesla transverse magnetic field. The residual resistance ratio (R200 K/R0 K) is found to be 3.64, which endorses the metallic nature of the synthesized crystal. The relative resistance turns out to be higher for the mixed TI than the pure TIs i.e., Bi$_2$Te$_3$ or Sb$_2$Te$_3$. The lower Debye temperature (82.64 K) of BiSbTe$_3$ connotes the presence of effective electron-phonon interaction at quite low temperatures in comparison to pure TI, which explains the observed suppression in magnetoresistance (MR) for the mixed TI. At 2 K, an MR of 150 percent is observed for BiSbTe$_3$, which is suppressed in contrast to the pure TIs i.e., Bi$_2$Te$_3$ or Sb$_2$Te$_3$. Though the MR% is suppressed significantly, its non-saturating linear behavior indicates the topological nature of the studied mixed TI. The modified Hikami-Larkin-Nagaoka (HLN) equation analysis of magneto-conductivity of mixed TI revealed that the conductivity has not only a surface states driven 2D component but also contributions from the bulk charge carriers and quantum scattering.

cond-mat.mtrl-sci↗
arXiv2025

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel

The mechanical properties and long-term structural reliability of crystalline materials are strongly influenced by microstructural features such as grain size, morphology, and crystallographic texture. These characteristics not only determine the initial mechanical behavior but also govern the progression of degradation mechanisms, such as strain localization, fatigue damage, and microcrack initiation under service conditions. Variability in these microstructural attributes, introduced during manufacturing or evolving through in-service degradation, leads to uncertainty in material performance. Therefore, understanding and quantifying microstructure-sensitive plastic deformation is critical for assessing degradation risk in high-value mechanical systems. This study presents a first-of-its-kind machine learning-driven framework that couples high-fidelity crystal plasticity finite element (CPFE) simulations with data-driven surrogate modeling to accelerate degradation-aware uncertainty quantification in welded structural alloys. Specifically, the impact of crystallographic texture variability in 316L stainless steel weldments, characterized via high-throughput electron backscatter diffraction (EBSD), is examined through CPFE simulations on calibrated representative volume elements (RVEs). A polynomial chaos expansion-based surrogate model is then trained to efficiently emulate the CPFE response using only 200 simulations, reducing computational cost by several orders of magnitude compared to conventional Monte Carlo analysis. The surrogate enables rapid quantification of uncertainty in stress-strain behavior and identifies texture components such as Cube and Goss as key drivers of degradation-relevant plastic response.

stat.AP↗
arXiv2025

Design and Implementation of a Dual Uncrewed Surface Vessel Platform for Bathymetry Research under High-flow Conditions

Bathymetry, the study of underwater topography, relies on sonar mapping of submerged structures. These measurements, critical for infrastructure health monitoring, often require expensive instrumentation. The high financial risk associated with sensor damage or vessel loss creates a reluctance to deploy uncrewed surface vessels (USVs) for bathymetry. However, the crewed-boat bathymetry operations, are costly, pose hazards to personnel, and frequently fail to achieve the stable conditions necessary for bathymetry data collection, especially under high currents. Further research is essential to advance autonomous control, navigation, and data processing technologies, with a particular focus on bathymetry. There is a notable lack of accessible hardware platforms that allow for integrated research in both bathymetry-focused autonomous control and navigation, as well as data evaluation and processing. This paper addresses this gap through the design and implementation of two complementary USV systems tailored for uncrewed bathymetry research. This includes a low-cost USV for Navigation And Control research (NAC-USV) and a second, high-end USV equipped with a high-resolution multi-beam sonar and the associated hardware for Bathymetry data quality Evaluation and Post-processing research (BEP-USV). The NAC-USV facilitates the investigation of autonomous, fail-safe navigation and control, emphasizing the stability requirements for high-quality bathymetry data collection while minimizing the risk to equipment. The BEP-USV, which mirrors the NAC-USV hardware, is then used for additional control validation and in-depth exploration of bathymetry data evaluation and post-processing methodologies. We detail the design and implementation of both systems, and open source the design. Furthermore, we demonstrate the system's effectiveness in a range of operational scenarios.

cs.RO↗
arXiv2025

New Physics effects with right-handed neutrinos in semileptonic decay $B_c^+ \to B_s μ^+ ν_μ$

We extend the Standard Model with the general effective Hamiltonian for the quark level transition $c \to s \ell ν$ with a complete set of four fermion operators including right-handed neutrinos. The current experimental measurements in charm decays are compatible with the Standard Model predictions and are used to constrain the new physics. With the available experimental data, we fit a $χ^2$ function to get the best-fit values of the NP WCs. We investigate the impact of allowed new physics in the observables such as differential branching fraction, forward-backward asymmetry, lepton polarization asymmetry, and convexity parameter in the semileptonic decay $B_c^+ \to B_s μ^+ ν_μ$. The different types of new physics scenarios have significant effects on these considered observables. The future experimental information of these observables can help to disentangle the structure of new physics.

hep-ph↗
arXiv2025

AI-driven Uncertainty Quantification & Multi-Physics Approach to Evaluate Cladding Materials in a Microreactor

The pursuit of enhanced nuclear safety has spurred the development of accident-tolerant cladding (ATC) materials for light water reactors (LWRs). This study investigates the potential of repurposing these ATCs in advanced reactor designs, aiming to expedite material development and reduce costs. The research employs a multi-physics approach, encompassing neutronics, heat transfer, thermodynamics, and structural mechanics, to evaluate four candidate materials (Haynes 230, Zircaloy-4, FeCrAl, and SiC-SiC) within the context of a high-temperature, sodium-cooled microreactor, exemplified by the Kilopower design. While neutronic simulations revealed negligible power profile variations among the materials, finite element analyses highlighted the superior thermal stability of SiC-SiC and the favorable stress resistance of Haynes 230. The high-temperature environment significantly impacted material performance, particularly for Zircaloy-4 and FeCrAl, while SiC-SiC's inherent properties limited its ability to withstand stress loads. Additionally, AI-driven uncertainty quantification and sensitivity analysis were conducted to assess the influence of material property variations on maximum hoop stress. The findings underscore the need for further research into high-temperature material properties to facilitate broader applicability of existing materials to advanced reactors. Haynes 230 is identified as the most promising candidate based on the evaluated criteria.

physics.ins-det↗
arXiv2025

Effect of Non-Extensive Parameter on Page Curve

This work employs the quantum extremal surface framework to compute the Page curve for black holes corrected by non-extensive entropy. The entropy of Hawking radiation increases linearly with time, leading to the persistence of the information paradox for non-extensive entropy-corrected black holes. At late time, we extremize the generalized entropy functional; incorporating contributions from both matter and the quantum extremal island, we establish that the entanglement entropy of Hawking radiation saturates to the non-extensive extension of the Bekenstein-Hawking entropy. Finally, we study the dependence of non-extensive parameters on the Page time.

hep-th↗
arXiv2024

UnMA-CapSumT: Unified and Multi-Head Attention-driven Caption Summarization Transformer

Image captioning is the generation of natural language descriptions of images which have increased immense popularity in the recent past. With this different deep-learning techniques are devised for the development of factual and stylized image captioning models. Previous models focused more on the generation of factual and stylized captions separately providing more than one caption for a single image. The descriptions generated from these suffer from out-of-vocabulary and repetition issues. To the best of our knowledge, no such work exists that provided a description that integrates different captioning methods to describe the contents of an image with factual and stylized (romantic and humorous) elements. To overcome these limitations, this paper presents a novel Unified Attention and Multi-Head Attention-driven Caption Summarization Transformer (UnMA-CapSumT) based Captioning Framework. It utilizes both factual captions and stylized captions generated by the Modified Adaptive Attention-based factual image captioning model (MAA-FIC) and Style Factored Bi-LSTM with attention (SF-Bi-ALSTM) driven stylized image captioning model respectively. SF-Bi-ALSTM-based stylized IC model generates two prominent styles of expression- {romance, and humor}. The proposed summarizer UnMHA-ST combines both factual and stylized descriptions of an input image to generate styled rich coherent summarized captions. The proposed UnMHA-ST transformer learns and summarizes different linguistic styles efficiently by incorporating proposed word embedding fastText with Attention Word Embedding (fTA-WE) and pointer-generator network with coverage mechanism concept to solve the out-of-vocabulary issues and repetition problem. Extensive experiments are conducted on Flickr8K and a subset of FlickrStyle10K with supporting ablation studies to prove the efficiency and efficacy of the proposed framework.

cs.CV↗
arXiv2024

On extension of the $_{r}R_{s,q}(α,β,z)$ function and their $q$-calculus

In this article, we investigate and establish some properties including analytic properties, contiguous relations, differential properties, differential operators, an expansion formula, and simple integrals, integral operators, some fractional integral properties, some new integral representations, the Riemann Liouville fractional $q$-derivative and $q$-integral operators of $q$-analogue of various basic $_{r}R_{s,q}$ function by using technique of $q$-calculus. Certain interesting consequences of the theorem are also discussed by considering some examples.

math.CA↗
arXiv2024

Evaluating Sugarcane Yield Variability with UAV-Derived Cane Height under Different Water and Nitrogen Conditions

This study investigates the relationship between sugarcane yield and cane height derived under different water and nitrogen conditions from pre-harvest Digital Surface Model (DSM) obtained via Unmanned Aerial Vehicle (UAV) flights over a sugarcane test farm. The farm was divided into 62 blocks based on three water levels (low, medium, and high) and three nitrogen levels (low, medium, and high), with repeated treatments. In pixel distribution of DSM for each block, it provided bimodal distribution representing two peaks, ground level (gaps within canopies) and top of the canopies respectively. Using bimodal distribution, mean cane height was extracted for each block by applying a trimmed mean to the pixel distribution, focusing on the top canopy points. Similarly, the extracted mean elevation of the base was derived from the bottom points, representing ground level. The Derived Cane Height Model (DCHM) was generated by taking the difference between the mean canopy height and mean base elevation for each block. Yield measurements (tons/acre) were recorded post-harvest for each block. By aggregating the data into nine treatment zones (e.g., high water-low nitrogen, low water-high nitrogen), the DCHM and median yield were calculated for each zone. The regression analysis between the DCHM and corresponding yields for the different treatment zones yielded an R 2 of 0.95. This study demonstrates the significant impact of water and nitrogen treatments on sugarcane height and yield, utilizing one-time UAV-derived DSM data.

cs.CV↗
arXiv2024

AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology

In response to the urgent need to establish AI/ML-integrated Digital Twin (DT) technology within next-generation nuclear systems, advancements in modeling methods and simulation codes are necessary. The increased complexity of models demands significant computational resources to quantify their uncertainties. To address this challenge, a data-driven non-intrusive uncertainty quantification method via polynomial chaos expansion is introduced as an efficient strategy within the finite element analysis-based fuel performance code BISON. Models of and fuels, alongside SiC/SiC cladding material, were prepared to demonstrate the proposed method. The impact of four independent uncertain input variables on the system output was quantified, requiring fewer than 100 BISON simulations for each model. This approach not only accelerates the modeling and simulation task but also enhances the reliability in the development of DT-enabling technologies.

stat.CO↗
arXiv2024

Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization

Deep learning applications for assessing medical images are limited because the datasets are often small and imbalanced. The use of synthetic data has been proposed in the literature, but neither a robust comparison of the different methods nor generalizability has been reported. Our approach integrates a retinal image quality assessment model and StyleGAN2 architecture to enhance Age-related Macular Degeneration (AMD) detection capabilities and improve generalizability. This work compares ten different Generative Adversarial Network (GAN) architectures to generate synthetic eye-fundus images with and without AMD. We combined subsets of three public databases (iChallenge-AMD, ODIR-2019, and RIADD) to form a single training and test set. We employed the STARE dataset for external validation, ensuring a comprehensive assessment of the proposed approach. The results show that StyleGAN2 reached the lowest Frechet Inception Distance (166.17), and clinicians could not accurately differentiate between real and synthetic images. ResNet-18 architecture obtained the best performance with 85% accuracy and outperformed the two human experts (80% and 75%) in detecting AMD fundus images. The accuracy rates were 82.8% for the test set and 81.3% for the STARE dataset, demonstrating the model's generalizability. The proposed methodology for synthetic medical image generation has been validated for robustness and accuracy, with free access to its code for further research and development in this field.

eess.IV↗
arXiv2023

Orientational dynamics of anisotropic colloidal particles in a planar extensional flow

Suspensions of anisotropic particles are commonly encountered in a wide spectrum of applications, including industrial and architectural coatings, targeted drug delivery and manufacturing of fiber-reinforced composites. A grand challenge in the field of chemical and material processing is robust production of strongly aligned fibers at the microscopic level, as this is routinely linked with enhanced mechanical properties at the macroscopic level. While the investigation of the microstructure of anisotropic colloids in shear flows has garnered a lot of theoretical and experimental attention, the case of extensional flow remains poorly understood due to several experimental challenges. In this article, we present a theoretical framework for predicting the steady and transient orientations of anisotropic particles in a flowing liquid undergoing precisely defined steady and time-dependent planar extensional flow at the stagnation point of a Stokes trap device. In particular, we analytically solve the Fokker-Planck equation for estimating the probability distribution function describing the orientation dynamics of rod-like objects as a function of flow strength (Peclet number, Pe) and probing frequency (Deborah number, De). The theoretical results are compared with recent experiments and reasonable agreement is found. We also discuss the challenges involved in obtaining a full closed-form solution for the transient dynamics of anisotropic particles in oscillatory time-dependent extensional flow. Overall, our theoretical framework provides a way to compare the orientation dynamics of rod-like particles with experiments that have been performed using a new experimental technique involving the Stokes trap and precise flow-control over the orientation of particles.

cond-mat.soft↗
arXiv2023

Impact of $b \to c$ measurements on $Λ_b \to p τ\barν$ decay in $U_1$ leptoquark model

The measurements of several lepton flavor universality (LFU) violating observables in the decays induced by the quark level transition $b \to c τ\barν$ provide an inkling of plausible physics beyond the standard model of electroweak interactions. Such new physics would also impact other sectors. In this work, we estimate the leverage of new physics in $b \to c τ\barν$ on $Λ_b \to p τ\barν$ decay in the context of $U_1$ leptoquark model. In this model, the new physics couplings in $b \to u τ\barν$ transition can be written in terms of $b \to c τ\barν$ couplings and hence the extent of allowed new physics in $Λ_b \to p τ\barν$ would be determined by $b \to c τ\barν$ transition. Using the new physics parameter space obtained by performing a fit to all $b \to c τ\barν$ data, we obtain predictions of several $Λ_b \to p τ\barν$ observables. We find that the current $b \to c τ\barν$ data allows an order of magnitude enhancement in the branching ratio as well as in the LFU ratio. The other observables such as convexity parameter, lepton forward-backward asymmetry, longitudinal polarization of final state baryon and tau lepton are consistent with the SM value.

hep-ph↗
arXiv2023

tomoCAM: Fast Model-based Iterative Reconstruction via GPU Acceleration and Non-Uniform Fast Fourier Transforms

X-Ray based computed tomography (CT) is a well-established technique for determining the three-dimensional structure of an object from its two-dimensional projections. In the past few decades, there have been significant advancements in the brightness and detector technology of tomography instruments at synchrotron sources. These advancements have led to the emergence of new observations and discoveries, with improved capabilities such as faster frame rates, larger fields of view, higher resolution, and higher dimensionality. These advancements have enabled the material science community to expand the scope of tomographic measurements towards increasingly in-situ and in-operando measurements. In these new experiments, samples can be rapidly evolving, have complex geometries, and restrictions on the field of view, limiting the number of projections that can be collected. In such cases, standard filtered back-projections (FBP) for the reconstructions often result in poor-quality reconstructions. Iterative reconstruction algorithms, such as model-based iterative reconstructions (MBIR), have demonstrated considerable success in producing high-quality reconstructions under such restrictions, but typically require high-performance computing resources with hundreds of compute nodes to solve the problem in a reasonable time.

physics.med-ph↗
arXiv2023

Dynamics on Bungee set, Escaping set and Filled Julia set of Composite Transcendental Entire Functions

In this paper, we have investigated the Bungee set of composition of two transcendental entire functions. We have provided a class of permutable entire functions for which their Bungee sets are equal. Moreover, we have obtained a result on permutable entire functions for which the escaping set of the composite entire function equals the union of the escaping sets of the two functions. In addition, we establish important relations between the Bungee set, Escaping set and Filled Julia set of composite entire functions with that of its individual functions.

math.DS↗
arXiv2022

Recent Advances in Uncertainty Quantification Methods for Engineering Problems

In the last few decades, uncertainty quantification (UQ) methods have been used widely to ensure the robustness of engineering designs. This chapter aims to detail recent advances in popular uncertainty quantification methods used in engineering applications. This chapter describes the two most popular meta-modeling methods for uncertainty quantification suitable for engineering applications (Polynomial Chaos Method and Gaussian Process). Further, the UQ methods are applied to an engineering test problem under multiple uncertainties. The test problem considered here is a supersonic nozzle under operational uncertainties. For the deterministic solution, an open-source computational fluid dynamics (CFD) solver SU2 is used. The UQ methods are developed in Matlab and are further combined with SU2 for the uncertainty and sensitivity estimates. The results are presented in terms of the mean and standard deviation of the output quantities.

stat.CO↗
arXiv2022

Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging: Path Forward to Digital Twin Enabling Simulation for Accident Tolerant Fuel

The Gaussian Process (GP)-based surrogate model has the inherent capability of capturing the anomaly arising from limited data, lack of data, missing data, and data inconsistencies (noisy/erroneous data) present in the modeling and simulation component of the digital twin framework, specifically for the accident tolerant fuel (ATF) concepts. However, GP will not be very accurate when we have limited high-fidelity (experimental) data. In addition, it is challenging to apply higher dimensional functions (>20-dimensional function) to approximate predictions with the GP. Furthermore, noisy data or data containing erroneous observations and outliers are major challenges for advanced ATF concepts. Also, the governing differential equation is empirical for longer-term ATF candidates, and data availability is an issue. Physics-informed multi-fidelity Kriging (MFK) can be useful for identifying and predicting the required material properties. MFK is particularly useful with low-fidelity physics (approximating physics) and limited high-fidelity data - which is the case for ATF candidates since there is limited data availability. This chapter explores the method and presents its application to experimental thermal conductivity measurement data for ATF. The MFK method showed its significance for a small number of data that could not be modeled by the conventional Kriging method. Mathematical models constructed with this method can be easily connected to later-stage analysis such as uncertainty quantification and sensitivity analysis and are expected to be applied to fundamental research and a wide range of product development fields. The overarching objective of this chapter is to show the capability of MFK surrogates that can be embedded in a digital twin system for ATF.

stat.CO↗