arXiv subjects

Vivek Pandey

Publications and source records attributed to Vivek Pandey.

At least 19 recordsLinked to original sources

arXiv2026

Skew scattering induced contribution to orbital Hall response

Our study provides the disorder-induced contribution to the orbital Hall conductivity in three-dimensional Weyl semimetals with broken time-reversal symmetry. Using the quantum kinetic approach, we analyse the impact of side-jump and skew scattering contributions to the system. The dependence of the orbital Hall conductivity on both disorder potential and the Fermi energy is explicitly demonstrated. Furthermore, we demonstrate that the higher-order disorder contribution, especially from the third power of disorder potential, dominates the orbital Hall conductivity under an oscillating electric field in a time-reversal symmetry broken Weyl semimetal, suppressing other scattering mechanisms, including the side jump contributions. We can enhance the extrinsic orbital Hall conductivity by tuning the strength of the disorder potential, applied energy, and choosing the system with appropriate Weyl node separation. Finally, our results are supported by numerical estimations and highlight potential experimental relevance for advancing orbitronics device technologies.

cond-mat.mes-hall↗

arXiv2026

Multi-Agent Next-Best-View Optimization for Risk-Averse Planning

Multi-agent Next-Best-View (NBV) selection for safe path planning in uncertain and unknown environments requires informative, safety-aware, and efficient coordination. Centralized approaches rely on sharing raw sensor data or significant communication overhead, resulting in limited scalability. We propose a distributed, risk-aware multi-agent NBV framework in which each robot maintains a private local 3D Gaussian Splatting map and the team jointly maximizes expected information gain (EIG) restricted to masked zones along planned trajectories. The resulting distributed objective is solved by Consensus ADMM (C-ADMM) over a communication graph, with each robot exchanging only candidate viewpoints, planned trajectory descriptors, and scalar EIG contributions. Collision risk along each trajectory is modeled via Average Value-at-Risk (AV@R) over the local 3DGS map and used both to shape the masking radius and to score planned paths. Experiments in Gibson environments at multiple team sizes show that the distributed formulation approaches the centralized baseline in mapping quality and trajectory safety while reducing communication by orders of magnitude.

cs.RO↗

arXiv2026

Conflict-Aware Active Perception and Control in 3D Gaussian Splatting Fields via Control Barrier Functions

Active perception in uncertain environments requires robots to navigate safely while acquiring informative observations to reduce map uncertainty. These objectives inherently conflict, as informative viewpoints often lie near uncertain regions with higher collision risk. To address this challenge, we develop a conflict-aware active perception and control framework for robotic systems operating in environments represented by 3D Gaussian Splatting (3DGS). Safety is enforced using a Control Barrier Function (CBF) derived from an Average Value-at-Risk AV@R collision-risk metric that accounts for geometric uncertainty and guarantees forward invariance of a safe set. To improve perception, we propose a risk-aware Expected Information Gain (EIG) formulation for selecting the next-best-view and introduce perception barrier functions that align the camera orientation with the local information-ascent direction. To obtain a tractable formulation for these conflicting safety and perception objectives, we propose a unified safety-critical, perception-aware quadratic program that enforces safety as a hard constraint while relaxing perception constraints through slack variables. Simulation results demonstrate that the proposed method improves both safety and information acquisition compared to existing 3DGS-based approaches.

cs.RO↗

arXiv2026

Entropic Value-at-Risk for Inter-Vehicle Collision in Platoons: Network- and Delay-Induced Bounds on Risk Due to Extreme Events

Safe operation of connected vehicle platoons under stochastic disturbances and time-delayed dynamics requires accurate quantification of rare but dangerous events, such as inter-vehicle collisions. We propose a rigorous framework for quantifying the risk of inter-vehicle collisions in connected vehicle platoons subject to time-delayed stochastic dynamics. We adopt the \emph{entropic value-at-risk} (EVaR) as a conservative metric to capture \emph{risk due to extreme events}, highlighting its advantages over conventional Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). By expressing the inter-vehicle distance covariance in terms of the Laplacian eigenvalues of the communication network, we derive \emph{network-and time-delay-induced bounds} on both the minimum inherent risk and the worst-case risk. Specifically, the algebraic connectivity dictates the maximum EVaR, while the largest Laplacian eigenvalue determines the minimum risk inherently induced by the network structure. Numerical simulations illustrate how network topology and time delay shape collision risk, offering actionable insights for the safe design of vehicle platoons operating under stochastic disturbances.

eess.SY↗

arXiv2026

Incremental Risk Assessment for Cascading Failures in Large-Scale Multi-Agent Systems

We develop a framework for studying and quantifying the risk of cascading failures in time-delay consensus networks, motivated by a team of agents attempting temporal rendezvous under stochastic disturbances and communication delays. To assess how failures at one or multiple agents amplify the risk of deviation across the network, we employ the Average Value-at-Risk as a systemic measure of cascading uncertainty. Closed-form expressions reveal explicit dependencies of the risk of cascading failure on the Laplacian spectrum, communication delay, and noise statistics. We further establish fundamental lower bounds that characterize the best-achievable network performance under time-delay constraints. These bounds serve as feasibility certificates for assessing whether a desired safety or performance goal can be achieved without exhaustive search across all possible topologies. In addition, we develop an efficient single-step update law that enables scalable propagation of conditional risk as new failures are detected. Analytical and numerical studies demonstrate significant computational savings and confirm the tightness of the theoretical limits across diverse network configurations.

eess.SY↗

arXiv2026

Interband response in spin-orbit coupled nodal line semimetals

This study investigates the interband conductivity for nodal line semimetals (NLSMs) in the presence of spin-orbit coupling (SOC), where the disorder reshapes the transport properties. The SOC breaks spin degeneracy, thus fundamentally altering the band dispersion and enabling multiple interband transport channels. Using a quantum kinetic framework, we analyze the interband conductivity originating from disorder-driven (extrinsic) and field-driven (intrinsic) mechanisms. We find that the interband response shows an anisotropic nature due to disorder driven counterparts. Additionally, our predictions show a tunable prominent transition peak arising from non-Pauli-blocked states that can be controlled via band parameters as well as external stimuli. To have an experimental relevance, we provide a numerical estimation for the interband response of TaAs using density functional theory estimated parameters. These results suggest the investigation of disorder-enabled signatures in spin systems.

cond-mat.mes-hall↗

arXiv2025

Diagonal Unitary Covariant Superchannels

We present a complete characterization of diagonal unitary covariant (DU-covariant) superchannels, i.e. higher-order transformations transforming quantum channels into themselves. Necessary and sufficient conditions for complete positivity and trace preservation are derived and the canonical decomposition describing DU-covariant superchannels is provided. The presented framework unifies and extends known families of covariant quantum channels and enables explicit analysis of their action on physically relevant examples, including amplitude-damping, bit-flip, and Pauli channels. Our results provide a practical toolbox for symmetry-restricted higher-order quantum processes and offer a setting for exploring open problems such as the celebrated positive partial transpose (PPT)-squared conjecture.

quant-ph↗

arXiv2025

Efficient Greedy Algorithms for Feature Selection in Robot Visual Localization

Robot localization is a fundamental component of autonomous navigation in unknown environments. Among various sensing modalities, visual input from cameras plays a central role, enabling robots to estimate their position by tracking point features across image frames. However, image frames often contain a large number of features, many of which are redundant or uninformative for localization. Processing all features can introduce significant computational latency and inefficiency. This motivates the need for intelligent feature selection, identifying a subset of features that are most informative for localization over a prediction horizon. In this work, we propose two fast and memory-efficient feature selection algorithms that enable robots to actively evaluate the utility of visual features in real time. Unlike existing approaches with high computational and memory demands, the proposed methods are explicitly designed to reduce both time and memory complexity while achieving a favorable trade-off between computational efficiency and localization accuracy.

cs.RO↗

arXiv2025

Distributionally Robust Cascading Risk in Multi-Agent Rendezvous: Extended Analysis of Parameter-Induced Ambiguity

Ensuring safety in autonomous multi-agent systems during time-critical tasks such as rendezvous is a fundamental challenge, particularly under communication delays and uncertainty in system parameters. In this paper, we develop a theoretical framework to analyze the \emph{distributionally robust risk of cascading failures} in multi-agent rendezvous, where system parameters lie within bounded uncertainty sets around nominal values. Using a time-delayed dynamical network as a benchmark model, we quantify how small deviations in these parameters impact collective safety. We introduce a \emph{conditional distributionally robust functional}, grounded in a bivariate Gaussian model, to characterize risk propagation between agents. This yields a \emph{closed-form risk expression} that captures the complex interaction between time delays, network structure, noise statistics, and failure modes. These expressions expose key sensitivity patterns and provide actionable insight for the design of robust and resilient multi-agent networks. Extensive simulations validate the theoretical results and demonstrate the effectiveness of our framework.

eess.SY↗

arXiv2025

Active Next-Best-View Optimization for Risk-Averse Path Planning

Safe navigation in uncertain environments requires planning methods that integrate risk aversion with active perception. In this work, we present a unified framework that refines a coarse reference path by constructing tail-sensitive risk maps from Average Value-at-Risk statistics on an online-updated 3D Gaussian-splat Radiance Field. These maps enable the generation of locally safe and feasible trajectories. In parallel, we formulate Next-Best-View (NBV) selection as an optimization problem on the SE(3) pose manifold, where Riemannian gradient descent maximizes an expected information gain objective to reduce uncertainty most critical for imminent motion. Our approach advances the state-of-the-art by coupling risk-averse path refinement with NBV planning, while introducing scalable gradient decompositions that support efficient online updates in complex environments. We demonstrate the effectiveness of the proposed framework through extensive computational studies.

cs.RO↗

arXiv2025

C-BerryTrans: A C++ code for first-principles calculation of Berry-curvature-driven anomalous Hall and Nernst conductivities

We present \textit{C-BerryTrans}, a C++ code for \textit{ab-initio} calculations of Berry-curvature-driven AHC \textit{i.e.}, $\sigma_{\mu \nu}^{AHC}$ and ANC \textit{i.e.}, $\alpha_{\mu \nu}^{ANC}$. The code extracts eigenvalues and momentum-matrix from WIEN2k calculations and evaluates the Berry curvature using a Kubo-like formalism. It parallelizes $\boldsymbol\Omega$ evaluation over \textbf{\textit{k}}-points and stores band-resolved curvature data in binary format. This design enables rapid post-processing of AHC and ANC over a wide range of temperature ($T$) and chemical potential ($\omega$) values in a single run. The code is benchmarked on ferromagnetic materials- Fe, Fe$_3$Ge, Pd, Fe$_3$Al, and Co$_2$FeAl. For Fe, the $\sigma_{xy}^{AHC}$ is obtained to be $\sim$775 ($\sim$744) $S/cm$ at 0 (300) $K$. For Fe$_3$Ge, the value of $\sigma_{xy}^{AHC}$ is 311 $S/cm$ at 300 $K$. Nextly, for Co$_2$FeAl, the computed value of $\sigma_{xy}^{AHC}$ at 2 $K$ is $\sim$56 $S/cm$. Moving further, magnitude of $\alpha_{xy}^{ANC}$ for Pd is found as $\sim$0.97 $AK^{-1}m^{-1}$ at 300 K. For Fe$_3$Al, the maximum magnitude of $\alpha_{xy}^{ANC}$ for $T\leq$500 $K$ is computed as $\sim$2.83 $AK^{-1}m^{-1}$. Lastly, for Co$_2$FeAl, the value of $\alpha_{xy}^{ANC}$ is found to be $\sim$0.10 $AK^{-1}m^{-1}$ at 300 $K$. These results show good agreement with the reported data. With its accuracy and user-friendly workflow, \textit{C-BerryTrans} provides a powerful tool for exploring $\boldsymbol\Omega$-driven transport and is well suited for high-throughput materials discovery. The code also enables the evaluation of $\boldsymbol\Omega$-derived AHC/ANC contributions along user-defined \textbf{\textit{k}}-point paths. Additionally, the code is equipped with a visualization module that allows analysis of \textbf{\textit{k}}-point contributions to AHC or ANC in any material.

cond-mat.mtrl-sci↗

arXiv2025

Distributionally Robust Cascading Risk Quantification in Multi-Agent Rendezvous: Effects of Time Delay and Network Connectivity

Achieving safety in autonomous multi-agent systems, particularly in time-critical tasks like rendezvous, is a critical challenge. In this paper, we propose a distributionally robust risk framework for analyzing cascading failures in multi-agent rendezvous. To capture the complex interactions between network connectivity, system dynamics, and communication delays, we use a time-delayed network model as a benchmark. We introduce a conditional distributionally robust functional to quantify cascading effects between agents, utilizing a bi-variate normal distribution. Our approach yields closed-form risk expressions that reveal the impact of time delay, noise statistics, communication topology, and failure modes on rendezvous risk. The insights derived inform the design of resilient networks that mitigate the risk of cascading failures. We validate our theoretical results through extensive simulations, demonstrating the effectiveness of our framework.

eess.SY↗

arXiv2025

Unveiling Novel Resonant Interband Contribution to Polarizability in three-dimensional systems

Polarizability plays an essential role in characterizing key phenomena, such as the screening effects, collective excitations, and dielectric functions present in the system. In three-dimensional materials, it typically comprises an intraband contribution, dependent on the chemical potential, and an interband contribution, largely independent of it. In this study, within the random phase approximation framework, we uncover a novel interband contribution that, unlike the conventional case, exhibits an explicit dependence on the chemical potential, which has no counterpart in two dimensions. In the long-wavelength limit, this term introduces a resonance feature with cubic wave-vector dependence when the chemical potential approaches the band edge, in contrast to the quadratic behavior characteristic of standard intraband and interband processes. Focusing on three-dimensional Dirac nodal line semimetals, we show that the polarizability is intraband-dominated at low frequencies, while interband processes prevail at intermediate and high frequencies, with the overall response being tunable via the chemical potential. Material-specific estimates for Ca$_3$P$_2$ and ZrSiS reveal a strong tunability of both contributions. These findings open new directions for probing frequency-dependent dielectric properties and hold promise for applications in tunable plasmonic and optoelectronic devices.

cond-mat.mes-hall↗

arXiv2025

Disorder induced dynamical interband response in Dirac nodal line semimetals

To obtain the total response of the system, the effect of disorder cannot be neglected, as it introduces a new contribution (i.e. extrinsic) in the total response of the system. In the study of dynamical (AC) effects, the interband response exhibits an exotic resonance peak due to interband transitions. Here, the dynamical interband response of Dirac nodal line semimetal is investigated by using the quantum kinetic approach. The scattering driven effect is analyzed under the first-order Born approximation (i.e., in the weak disorder limit) and reveals a resonance peak at $2\tilde{\mu}$. In contrast, the field driven intrinsic response peak depends on both the mass ($\tilde{M}$) and chemical potential ($\tilde{\mu}$). The results indicate that the total interband response of the 3D nodal line semimetals, is mainly dominated by the disorder induced contributions.

cond-mat.mes-hall↗

arXiv2025

Reshaping the anomalous Hall response in tilted 3D system with disorder correction

The anomalous Hall conductivity in the nodal line semimetals (NLSMs) due to the presence of a symmetry-protected nodal ring adds complexity in the investigation of their transport properties. By employing quantum kinetic theory and considering the weak disorder limit, we analyze the intraband and interband parts of anomalous Hall conductivity in the tilted 3D Dirac NLSMs. Our findings reveal that the net anomalous response is mainly contributed by the interband part. Further, the latter part gives non zero results by breaking inversion symmetry via tilt. We observe that the competition between the tilt and the chemical potential emerges kinks at distinct characteristic frequencies in the intrinsic interband part of the anomalous conductivity. On the other hand, the disorder driven interband component of the conductivity exhibits a prominent peak at low chemical potential, followed by a sign change. Notably, the disorder or extrinsic contribution to the response dominates over the intrinsic interband contribution, making it a crucial factor for the study of the overall response of a three-dimensional system.

cond-mat.mes-hall↗

arXiv2025

C-BerryANC: A first-principle C++ code to calculate Berry Curvature dependent anomalous Nernst conductivity in any material

The anomalous Nernst conductivity (ANC) is a key transport property in magnetic and topological materials, arising from the Berry curvature ($\boldsymbol\Omega$) of electronic bands. It offers deep insight into the underlying topology and thermoelectric behavior. While Wannier interpolation have become popular for calculating ANC due to their computational efficiency, their accuracy critically depends on the quality of the Wannierization, which can be challenging for entangled bands or materials with complex band crossings. These limitations highlight the need for a direct first-principles approach to reliably compute ANC from ab-initio electronic structures. Here, we present a C++ based code named C-BerryANC that calculates $\boldsymbol\Omega$-dependent ANC by directly using the eigenvalues and momentum-matrices obtained from DFT calculations. Presently, the code is interfaced with WIEN2k package which uses all-electron approach and full-potential linearized augmented plane wave (FP-LAPW) based method. For efficiently handling dense k-mesh, calculation of $\boldsymbol\Omega$ is made parallel over k-points using the OpenMP method. Additionally, the code stores band-resolved components of $\boldsymbol\Omega$ in binary files thereby reducing the memory occupancy and providing fast post-process option to compute ANC for any range of chemical potential and temperature values. Also, as compilation of C++ modules produce executable files which are in machine level language, computational speed of C-BerryANC is very fast. The code is benchmarked over some well-known materials exhibiting ANC. These includes- Pd, Fe$_3$Al & Co$_2$FeAl. The obtained values of ANC is found to in good agreement with the previously reported data. This highlights the accuracy, efficieny and reliability of the C-BerryANC code.

cond-mat.mtrl-sci↗

arXiv2025

Tunable optical bistability of two-dimensional tilted Dirac system

We study the phenomenon of controlling the light by light known as the optical bistability for the two-dimensional tilted Dirac system. Using the Boltzmann approach under relaxation time approximation, we find that the optical bistability can be controlled by the nonlinear response of the system. For the prototype, we consider an inversion symmetry broken system. We find that the optical bistability associated with the nonlinear response is tunable with the strength of the tilt, gap and chemical potential. This suggests the inputs for the development of future-generation optical devices.

physics.optics↗

arXiv2025

$\textit{PY-BerryAHC}$: An $\textit{ab-initio}$ python 3 code to calculate Berry Curvature dependent Anomalous Hall Conductivity in any material

The anomalous Hall conductivity (AHC) in materials has long been a topic of debate. Studies reveal that AHC originates from the Berry curvature ($\boldsymbol\Omega$) of Bloch states. Accurate computation of AHC is crucial for predicting material properties and guiding experimental studies in topological and spintronic applications. Traditional approaches often rely on wannier interpolation, which can introduce inaccuracies and computational overhead. Also, reliability of the wannierization technique becomes questionable when the bands are highly entangled and dispersive. This demands the calculation of AHC using the $\textit{first-principle}$ approach. Here, we present $\textit{PY-BerryAHC}$, a Python 3 based code that directly computes $\boldsymbol\Omega$ and AHC using WIEN2k output. Since, WIEN2k employs an all-electron full-potential linearized augmented plane wave method, $\textit{PY-BerryAHC}$ provides highly accurate AHC results. The code efficiently handles large $\textbf{k}$-grids by parallelizing $\boldsymbol\Omega$ computations over $\textbf{k}$-points. Also, it stores band-resolved $\boldsymbol\Omega$ in a binary file, thereby greatly reducing the required storage memory and allowing fast post-processing to compute AHC. $\textit{PY-BerryAHC}$ has been validated on well-known materials exhibiting AHC. These include- Fe, Fe$_3$Ge & Co$_2$FeAl. At 300 K, the calculated magnitude of $\sigma_{xy}$ for Fe & Fe$_3$Ge is found to be 744 $S/cm$ & 311 $S/cm$, respectively. For Co$_2$FeAl, the magnitude of $\sigma_{xy}$ is obtained to be $\sim$56 $S/cm$ and is found to be constant with the change in temperature from 0-300 K. These results are in good agreement with previously reported theoretical and experimental data. This ensures the accuracy, reliability and efficiency of the code. The code is also provided with a post-processing tool to visualize $\boldsymbol\Omega$.

cond-mat.str-el↗