arXiv subjects

Himanshu Sharma

Publications and source records attributed to Himanshu Sharma.

At least 19 recordsLinked to original sources

arXiv2026

Search for intranight optical variability in a large sample of intermediate-mass black holes

Using the extensive archival database of the Zwicky Transient Facility (ZTF) survey, we have investigated intranight optical variability (INOV) in a large sample of low-mass active galactic nuclei (LMAGN) powered by intermediate-mass black holes (IMBHs). After applying a sequence of well-motivated selection filters, we constructed an unbiased, representative sample of 62 IMBHs for which $r$-band intranight photometric sequences are available in the ZTF database, yielding 167 sessions with a minimum duration of 2 h. By performing aperture photometry on the sequence of exposures in each session, we derived differential light curves (DLCs) of the target LMAGN relative to two carefully selected non-varying comparison stars monitored simultaneously with the LMAGN in the same exposure sequence. Application of the widely used test based on $F$-statistics to all these DLCs revealed no case of statistically significant INOV. We discuss this result for the present large sample of LMAGN in the context of an earlier report of INOV detections in another, albeit much smaller and differently selected, sample of LMAGN that had been monitored in targeted observations.

astro-ph.GA↗

arXiv2026

Thermoelectric properties of Topological Weyl Semimetal Cu$_2$ZnGeTe$_4$

The exploration of topological quantum materials for thermoelectric (TE) applications offers an opportunity to combine nontrivial electronic topology with efficient energy conversion. Topological semimetals (TSMs), including Dirac, Weyl, and nodal-line systems, can exhibit favourable transport properties arising from the coexistence of dispersive and relatively flat bands near the Fermi level (E_F). However, their gapless electronic structure can also suppress thermopower and enhance electronic thermal conductivity, limiting the overall TE performance. Here, we combine first-principles calculations and transport measurements to investigate Cu2ZnGeTe4 as a lattice-tunable platform connecting thermoelectric and topological electronic phases. At the experimentally measured lattice parameters, Cu2ZnGeTe4 is a narrow-gap semiconductor with a calculated band gap of approximately 0.067 eV and a maximum calculated ZT of approximately 1. Experimentally, the compound exhibits p-type semiconducting behavior and low lattice thermal conductivity, yielding a ZT of approximately 0.14 at 623 K and reproducing the calculated temperature-dependent transport trends. Upon lattice expansion, first-principles calculations predict band inversion and a transition to a Weyl-semimetallic phase with Weyl nodes of opposite chirality and topological surface states. Although this phase exhibits enhanced electrical conductivity and a high power factor, its maximum ZT of approximately 0.36 remains lower than that of the semiconducting phase because of concomitant increases in electronic and lattice thermal conductivities. Isovalent Sn substitution at the Ge site reproduces the essential electronic features of the expanded phase, providing a possible chemical route toward the predicted topologically nontrivial state.

cond-mat.mtrl-sci↗

arXiv2026

On dynamical irreducible set of polynomials

In this article, a necessary and sufficient condition is proved for the dynamical irreducibility of a family of polynomials over a finite field. Using this result, an explicit construction of a dynamically irreducible set of polynomials is given over the finite field $\mathbb{F}_{p^p}$ where $p$ is a prime. Moreover, the existence of dynamically irreducible sets of size at least $p^2$ is also established over every finite field $\mathbb{F}_q$ where $q$ is a $p$ power. Finally, a bound on the cardinality of the set is given that needs to be tested for the dynamical irreducibility of a set of polynomials.

math.NT↗

arXiv2026

Dimensional Confinement Driven Scattering Inversion in NaCrTe$_2$

Dimensionality reduction provides a powerful route to tune the electronic and magnetic properties of van der Waals materials, yet its influence on electronic transport remains complex due to competing effects from quantum confinement and modified scattering mechanisms. Here, we investigate this interplay in an antiferromagnetic semiconductor $\text{NaCrTe}_2$ using first-principles calculations combined with the Boltzmann transport equation beyond the constant relaxation time approximation. Our results show that the monolayer limit induces a coupled magnetostructural reconstruction, reducing the band gap from $0.44$ eV (bulk) to $0.15$ eV (monolayer) and significantly enhancing the static dielectric constant. This evolution triggers a fundamental scattering inversion: whereas bulk transport is limited by polar optical phonon (POP) scattering, the monolayer becomes dominated by acoustic deformation potential (ADP) scattering. We show that this crossover originates from the simultaneous suppression of the Fröhlich interaction through enhanced dielectric screening and the amplification of acoustic scattering due to pronounced lattice softening. These results clarify how the interplay between dielectric screening, lattice stiffness, and band topology governs transport in low-dimensional magnetic semiconductors, providing a framework to optimize their electronic performance.

cond-mat.mtrl-sci↗

arXiv2026

STREAM: Multi-Tier LLM Inference Middleware with Dual-Channel HPC Token Streaming

Researchers and practitioners working with large language models face a fragmented landscape: local models are free and private but hardware limits the model size and context windows a researcher can use; institutional HPC centers offer powerful GPU resources at no marginal cost and keep data within institutional boundaries, but operate behind firewalls and are designed for batch jobs rather than interactive use; commercial cloud APIs provide frontier-model quality on demand but impose significant cost and data retention policies unsuitable for sensitive research data. No existing system unifies all three. STREAM (Smart Tiered Routing Engine for AI Models) addresses this gap with four contributions: (1) a three-tier routing architecture combining local, HPC, and cloud inference with a local LLM-based complexity judge; (2) a dual-channel HPC streaming architecture that separates the Globus Compute control plane (authentication and job dispatch) from a WebSocket relay data plane (token delivery), enabling sub-second TTFT (0.54 s median, 21.1x over batch mode's 11.40 s) through institutional firewalls without VPN or firewall rule changes, with end-to-end AES-256-GCM encryption ensuring the relay operator cannot read token payloads; (3) tier-aware context summarization that prevents long conversations from forcing simple queries onto expensive tiers; and (4) an HPC-as-API proxy mode that exposes HPC inference as an OpenAI-compatible endpoint callable from any standard client with no HPC expertise, a deployment pattern made practical only by the sub-second TTFT of contribution (2). Llama 3.2 3B achieves 85.1% free-tier retention on a 1,200-query benchmark spanning ten domains. Measured TTFT: 0.26 s local, 0.54 s HPC (relay), 1.68 s cloud.

cs.DC↗

arXiv2026

Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves

A physics-constrained Gaussian Process regression framework is developed for predicting shocked material states and their associated uncertainties along the Hugoniot curve using data from a small number of shockwave simulations. The proposed Gaussian process is constrained by the Rankine-Hugoniot jump conditions between the various shocked material states to construct a thermodynamically consistent covariance function. This leads to the formulation of an optimization problem over a small number of interpretable hyperparameters and enables the identification of regime transitions, from a leading elastic wave to trailing plastic and phase transformation waves. Shock Hugoniots are an important measure for understanding material behavior under extreme conditions, including for the development of equations of state and determining material properties such as the Hugoniot Elastic Limit, but they are costly to generate through large-scale molecular dynamics simulations or shock experiments. Under these constraints, the proposed methodology establishes Hugoniot curves from a limited number of molecular dynamics simulations. We consider silicon carbide as a representative material and Molecular Dynamics simulations are performed using a reverse ballistic approach. The framework reproduces the Hugoniot curve with satisfactory accuracy while also quantifying the uncertainty in the predictions using the Gaussian Process posterior. These uncertain Hugoniot predictions can then be used to calibrate equation of state models, estimate material properties, or inform future experimental and/or simulation campaigns.

cs.CE↗

arXiv2026

Topological Weyl Phase of an Ideal Spin-Gapless Semiconductor KCrSe

The coexistence of topological and spin-polarized electronic states within a single material platform provides an attractive route toward emergent quantum phenomena and spintronic functionalities. However, materials simultaneously exhibiting spin-gapless semiconducting (SGS) behavior and Weyl semimetallicity remain exceedingly rare. Here, using first-principles calculations, we identify the half-Heusler compound KCrSe as an ideal spin-gapless Weyl semimetal. Transport calculations reveal a weak temperature dependence of the longitudinal conductivity and relatively small Seebeck coefficients, providing further evidence of its SGS nature. KCrSe hosts a single pair of Weyl nodes-the minimum number permitted in a Weyl semimetal-located in close proximity to the Fermi level (E$_\text{F}$), resulting in exceptionally clean bulk and surface electronic spectra. The nontrivial Berry curvature associated with these Weyl nodes gives rise to sizable anomalous transport responses, including an anomalous Hall conductivity of $σ_{xy}^{A}\sim 90.76~\mathrm{S\,cm^{-1}}$ and an anomalous Nernst conductivity of $α_{xy}^{A}\sim 0.15~\mathrm{A\,m^{-1}K^{-1}}$ at E$_\text{F}$, with substantially enhanced values at lower energies. The combination of an ideal Weyl topology, fully spin-polarized low-energy states, and finite anomalous transport establishes KCrSe as a promising platform for designing high-efficiency topological spintronic devices.

cond-mat.mtrl-sci↗

arXiv2026

AI in the Enterprise: How People Use M365 Copilot Chat

M365 Copilot is used every week by millions of people across more than a million companies around the world as part of their workflows. Uniquely positioned in the AI landscape given its near-exclusive use for work purposes, M365 Copilot can offer a clear picture of how people use AI for work and where that usage may expand next. This paper characterizes that usage through direct classification of user interactions with M365 Copilot Chat. Based on an anonymized and privacy-preserving analysis of a sample of approximately 5.5 million sessions, we combine a learned classification of user intent with a classification of O*NET work activities done with M365 Copilot Chat. We find that M365 Copilot is emerging as an everyday assistant for knowledge work: writing dominates, but users also rely on it for information retrieval, analysis, decision making and strategizing, and evaluating and diagnosing programs and systems, among others. Information seeking tasks remain common, but time trends suggest a relative shift away from ``chat as search'' and toward content and communication-related work. Comparisons across occupational groupings and to work done in the labor market further show that usage is broad but uneven, where the relative share of work done with M365 Copilot Chat cuts across jobs in some cases and is occupation-specific in others. Areas of relative underrepresentation in the labor market suggest the next frontier for enterprise AI adoption.

cs.CY↗

arXiv2026

LIME-LLM: Probing Models with Fluent Counterfactuals, Not Broken Text

Local explanation methods such as LIME (Ribeiro et al., 2016) remain fundamental to trustworthy AI, yet their application to NLP is limited by a reliance on random token masking. These heuristic perturbations frequently generate semantically invalid, out-of-distribution inputs that weaken the fidelity of local surrogate models. While recent generative approaches such as LLiMe (Angiulli et al., 2025b) attempt to mitigate this by employing Large Language Models for neighborhood generation, they rely on unconstrained paraphrasing that introduces confounding variables, making it difficult to isolate specific feature contributions. We introduce LIME-LLM, a framework that replaces random noise with hypothesis-driven, controlled perturbations. By enforcing a strict "Single Mask-Single Sample" protocol and employing distinct neutral infill and boundary infill strategies, LIME-LLM constructs fluent, on-manifold neighborhoods that rigorously isolate feature effects. We evaluate our method against established baselines (LIME, SHAP, Integrated Gradients) and the generative LLiMe baseline across three diverse benchmarks: CoLA, SST-2, and HateXplain using human-annotated rationales as ground truth. Empirical results demonstrate that LIME-LLM establishes a new benchmark for black-box NLP explainability, achieving significant improvements in local explanation fidelity compared to both traditional perturbation-based methods and recent generative alternatives.

cs.CL↗

arXiv2026

Physics-Informed Gaussian Process Regression for the Constitutive Modeling of Concrete: A Data-Driven Improvement to Phenomenological Models

Understanding and modeling the constitutive behavior of concrete is crucial for civil and defense applications, yet widely used phenomenological models such as Karagozian \& Case concrete (KCC) model depend on empirically calibrated failure surfaces that lack flexibility in model form and associated uncertainty quantification. This work develops a physics-informed framework that retains the modular elastoplastic structure of KCC model while replacing its empirical failure surface with a constrained Gaussian Process Regression (GPR) surrogate that can be learned directly from experimentally accessible observables. Triaxial compression data under varying confinement levels are used for training, and the surrogate is then evaluated at confinement levels not included in the training set to assess its generalization capability. Results show that an unconstrained GPR interpolates well near training conditions but deteriorates and violates essential physical constraints under extrapolation, even when augmented with simulated data. In contrast, a physics-informed GPR that incorporates derivative-based constraints aligned with known material behavior yields markedly better accuracy and reliability, including at higher confinement levels beyond the training range. Probabilistic enforcement of these constraints also reduces predictive variance, producing tighter confidence intervals in data-scarce regimes. Overall, the proposed approach delivers a robust, uncertainty-aware surrogate that improves generalization and streamlines calibration without sacrificing the interpretability and numerical efficiency of the KCC model, offering a practical path toward an improved constitutive models for concrete.

cs.LG↗

arXiv2025

Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Sampling

Physics-informed polynomial chaos expansions (PC$^2$) provide an efficient physically constrained surrogate modeling framework by embedding governing equations and other physical constraints into the standard data-driven polynomial chaos expansions (PCE) and solving via the Karush-Kuhn-Tucker (KKT) conditions. This approach improves the physical interpretability of surrogate models while achieving high computational efficiency and accuracy. However, the performance and efficiency of PC$^2$ can still be degraded with high-dimensional parameter spaces, limited data availability, or unrepresentative training data. To address this problem, this study explores two complementary enhancements to the PC$^2$ framework. First, a numerically efficient constrained optimization solver, straightforward updating of Lagrange multipliers (SULM), is adopted as an alternative to the conventional KKT solver. The SULM method significantly reduces computational cost when solving physically constrained problems with high-dimensionality and derivative boundary conditions that require a large number of virtual points. Second, a D-optimal sampling strategy is utilized to select informative virtual points to improve the stability and achieve the balance of accuracy and efficiency of the PC$^2$. The proposed methods are integrated into the PC$^2$ framework and evaluated through numerical examples of representative physical systems governed by ordinary or partial differential equations. The results demonstrate that the enhanced PC$^2$ has better comprehensive capability than standard PC$^2$, and is well-suited for high-dimensional uncertainty quantification tasks.

stat.ML↗

arXiv2025

Do Low-Mass, Low-Luminosity AGNs Deviate from the Quasar Main Sequence?

We present a comprehensive spectroscopic and variability-based characterisation of a sample of low-luminosity active galactic nuclei (AGNs) hosting low mass black holes, identified by $Hβ$ full width at half maximum (FWHM) $< 2200$ km s$^{-1}$. While the narrow line widths are consistent with the formal definition of narrow-line Seyfert 1 (NLSy1) galaxies, the broader accretion and emission properties reveal key distinctions. The sample exhibits sub-Eddington accretion rates (median $\log R_{Edd} \approx -0.68$) and comparatively weak FeII emission (median $R_{FeII} \approx 0.61$), in contrast to the strong FeII strengths and high Eddington ratios characteristic of classical NLSy1s. Optical variability amplitudes, derived from Zwicky Transient Facility (ZTF) light curves, are similar to those typically seen in Seyfert 1 galaxies, with a median $\log(σ) \approx -0.68$, suggesting the AGN component's significant contribution to variability. In the optical plane of the 4D Eigenvector 1 (4DE1) parameter space, these sources occupy a distinct locus in the low-$R_{FeII}$, low-$R_{Edd}$ regime, suggesting a physically distinct accretion state. Our findings indicate that this population may represent a low-accretion analogue within the broader narrow-line AGN family, offering new insights into black hole growth at low mass scales.

astro-ph.GA↗

arXiv2025

Learning to Solve Constrained Bilevel Control Co-Design Problems

Learning to Optimize (L2O) is a subfield of machine learning (ML) in which ML models are trained to solve parametric optimization problems. The general goal is to learn a fast approximator of solutions to constrained optimization problems, as a function of their defining parameters. Prior L2O methods focus almost entirely on single-level programs, in contrast to the bilevel programs, whose constraints are themselves expressed in terms of optimization subproblems. Bilevel programs have numerous important use cases but are notoriously difficult to solve, particularly under stringent time demands. This paper proposes a framework for learning to solve a broad class of challenging bilevel optimization problems, by leveraging modern techniques for differentiation through optimization problems. The framework is illustrated on an array of synthetic bilevel programs, as well as challenging control system co-design problems, showing how neural networks can be trained as efficient approximators of parametric bilevel optimization.

math.OC↗

arXiv2025

Control Affine Hybrid Power Plant Subsystem Modeling for Supervisory Control Design

Hybrid power plants (HPPs) combine multiple power generators (conventional/variable) and energy storage capabilities to support generation inadequacy and grid demands. This paper introduces a modeling and control design framework for hybrid power plants (HPPs) consisting of a wind farm, solar plant, and battery storage. Specifically, this work adapts established modeling paradigms for wind farms, solar plants and battery models into a control affine form suitable for control design at the supervisory level. In the case of wind and battery models, generator torque and cell current control laws are developed using nonlinear control and control barrier function techniques to track a command from a supervisory control law while maintaining safe and stable operation. The utility of this modeling and control framework is illustrated through a test case using a utility demand signal for tracking, time varying wind and irradiance data, and a rule-based supervisory control law.

eess.SY↗

arXiv2025

Supervisory Control of Hybrid Power Plants Using Online Feedback Optimization: Designs and Validations with a Hybrid Co-Simulation Engine

This research investigates designing a supervisory feedback controller for a hybrid power plant that coordinates the wind, solar, and battery energy storage plants to meet the desired power demands. We have explored an online feedback control design that does not require detailed knowledge about the models, known as feedback optimization. The control inputs are updated using the gradient information of the cost and the outputs with respect to the input control commands. This enables us to adjust the active power references of wind, solar, and storage plants to meet the power generation requirements set by grid operators. The methodology also ensures robust control performance in the presence of uncertainties in the weather. In this paper, we focus on describing the supervisory feedback optimization formulation and control-oriented modeling for individual renewable and storage components of the hybrid power plant. The proposed supervisory control has been integrated with the hybrid plant co-simulation engine, Hercules, demonstrating its effectiveness in more realistic simulation scenarios.

eess.SY↗

arXiv2025

Polynomial Chaos Expansion for Operator Learning

Operator learning (OL) has emerged as a powerful tool in scientific machine learning (SciML) for approximating mappings between infinite-dimensional functional spaces. One of its main applications is learning the solution operator of partial differential equations (PDEs). While much of the progress in this area has been driven by deep neural network-based approaches such as Deep Operator Networks (DeepONet) and Fourier Neural Operator (FNO), recent work has begun to explore traditional machine learning methods for OL. In this work, we introduce polynomial chaos expansion (PCE) as an OL method. PCE has been widely used for uncertainty quantification (UQ) and has recently gained attention in the context of SciML. For OL, we establish a mathematical framework that enables PCE to approximate operators in both purely data-driven and physics-informed settings. The proposed framework reduces the task of learning the operator to solving a system of equations for the PCE coefficients. Moreover, the framework provides UQ by simply post-processing the PCE coefficients, without any additional computational cost. We apply the proposed method to a diverse set of PDE problems to demonstrate its capabilities. Numerical results demonstrate the strong performance of the proposed method in both OL and UQ tasks, achieving excellent numerical accuracy and computational efficiency.

stat.ML↗

arXiv2025

Learning Neural Differential Algebraic Equations via Operator Splitting

Differential algebraic equations (DAEs) describe the temporal evolution of systems that obey both differential and algebraic constraints. Of particular interest are systems that contain implicit relationships between their components, such as conservation laws. Here, we present an Operator Splitting (OS) numerical integration scheme for learning unknown components of DAEs from time-series data. In this work, we show that the proposed OS-based time-stepping scheme is suitable for relevant system-theoretic data-driven modeling tasks. Presented examples include (i) the inverse problem of tank-manifold dynamics and (ii) discrepancy modeling of a network of pumps, tanks, and pipes. Our experiments demonstrate the proposed method's robustness to noise and extrapolation ability to (i) learn the behaviors of the system components and their interaction physics and (ii) disambiguate between data trends and mechanistic relationships contained in the system.

cs.LG↗

arXiv2025

Control Co-Design Under Uncertainty for Offshore Wind Farms: Optimizing Grid Integration, Energy Storage, and Market Participation

Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. This paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2\% and provide flexibility in managing wind resource uncertainties.

eess.SY↗