arXiv subjects

Kalp Pandya

Publications and source records attributed to Kalp Pandya.

3 recordsLinked to original sources

arXiv2026

Assessing the impact of Open Research Information Infrastructures using NLP driven full-text Scientometrics: A case study of the LXCat open-access platform

Open research information (ORI) play a central role in shaping how scientific knowledge is produced, disseminated, validated, and reused across the research lifecycle. While the visibility of such ORI infrastructures is often assessed through citation-based metrics, in this study, we present a full-text, natural language processing (NLP) driven scientometric framework to systematically quantify the impact of ORI infrastructures beyond citation counts, using the LXCat platform for low temperature plasma (LTP) research as a representative case study. The modeling of LTPs and interpretation of LTP experiments rely heavily on accurate data, much of which is hosted on LXCat, a community-driven, open-access platform central to the LTP research ecosystem. To investigate the scholarly impact of the LXCat platform over the past decade, we analyzed a curated corpus of full-text research articles citing three foundational LXCat publications. We present a comprehensive pipeline that integrates chemical entity recognition, dataset and solver mention extraction, affiliation based geographic mapping and topic modeling to extract fine-grained patterns of data usage that reflect implicit research priorities, data practices, differential reliance on specific databases, evolving modes of data reuse and coupling within scientific workflows, and thematic evolution. Importantly, our proposed methodology is domain-agnostic and transferable to other ORI contexts, and highlights the utility of NLP in quantifying the role of scientific data infrastructures and offers a data-driven reflection on how open-access platforms like LXCat contribute to shaping research directions. This work presents a scalable scientometric framework that has the potential to support evidence based evaluation of ORI platforms and to inform infrastructure design, governance, sustainability, and policy for future development.

physics.plasm-ph↗

arXiv2025

Hybrid Fourier Neural Operator-Plasma Fluid Model for Fast and Accurate Multiscale Simulations of High Power Microwave Breakdown

Modeling and simulation of High Power Microwave (HPM) breakdown, a multiscale phenomenon, is computationally expensive and requires solving Maxwell's equations (EM solver) coupled with a plasma continuity equation (plasma solver). In this work, we present a hybrid modeling approach that combines the accuracy of a differential equation-based plasma fluid solver with the computational efficiency of FNO (Fourier Neural Operator) based EM solver. Trained on data from an in-house FDTD-based plasma-fluid solver, the FNO replaces computationally expensive EM field updates, while the plasma solver governs the dynamic plasma response. The hybrid model is validated on microwave streamer formation, due to diffusion ionization mechanism, in a 2D scenario for unseen incident electric fields corresponding to entirely new plasma streamer simulations not included in model training, showing excellent agreement with FDTD based fluid simulations in terms of streamer shape, velocity, and temporal evolution. This hybrid FNO based strategy delivers significant acceleration of the order of 60X compared to traditional simulations for the specified problem size and offers an efficient alternative for computationally demanding multiscale and multiphysics simulations involved in HPM breakdown. Our work also demonstrate how such hybrid pipelines can be used to seamlessly to integrate existing C-based simulation codes with Python-based machine learning frameworks for simulations of plasma science and engineering problems.

physics.plasm-ph↗

arXiv2024

On Naisargik Images of Varshamov-Tenengolts and Helberg Codes

The VT and Helberg codes, both in binary and non-binary forms, stand as elegant solutions for rectifying insertion and deletion errors. In this paper we consider the quaternary versions of these codes. It is well known that many optimal binary non-linear codes like Kerdock and Prepreta can be depicted as Gray images (isometry) of codes defined over $\mathbb{Z}_4$. Thus a natural question arises: Can we find similar maps between quaternary and binary spaces which gives interesting properties when applied to the VT and Helberg codes. We found several such maps called Naisargik (natural) maps and we study the images of quaternary VT and Helberg codes under these maps. Naisargik and inverse Naisargik images gives interesting error-correcting properties for VT and Helberg codes. If two Naisargik images of VT code generates an intersecting one deletion sphere, then the images holds the same weights. A quaternary Helberg code designed to correct $s$ deletions can effectively rectify $s+1$ deletion errors when considering its Naisargik image, and $s$-deletion correcting binary Helberg code can corrects $\lfloor\frac{s}{2}\rfloor$ errors with inverse Naisargik image.

cs.IT↗