arXiv subjects

Atul Kumar

Publications and source records attributed to Atul Kumar.

At least 19 recordsLinked to original sources

arXiv2026

RF-Specific Tungsten Erosion and Global Transport in ITER under Neon Seeding

Ion cyclotron radio-frequency heating (ICRH) is a key auxiliary heating system in ITER, but high-power RF operation can enhance plasma-material interactions through rectified RF sheath potentials on antenna structures and nearby plasma-facing components. We present the first predictive application of the STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to assess RF sheath-driven tungsten (W) erosion and global impurity transport from the ITER ICRH antenna under ITER-relevant neon-seeded conditions. STRIPE couples SOLPS-ITER plasma backgrounds, full-wave RF sheath calculations, geometry-specific ion energy-angle distributions, sputtering physics, and three-dimensional impurity transport. Simulations predict RF sheath potentials of 1 to 3 kV on antenna limiter sidewalls, increasing gross W erosion by about a factor of 64 relative to thermal sheath conditions and producing a gross source of 3.34e18 W atoms per second. Erosion is governed by RF-modified ion energy-angle distributions together with local plasma flux rather than sheath voltage alone. About 10 percent of sputtered W is locally redeposited, giving a net source of 3.01e18 W atoms per second. The RF-induced antenna source remains about three orders of magnitude smaller than the thermal divertor source and more than two orders of magnitude smaller than the integrated thermal main-chamber source. After 100 ms, about 22 percent of the mobile W inventory resides within the SOLPS-covered confined-plasma region, corresponding to an annular W concentration of 1.70e-6. These results indicate that the ITER ICRH antenna is unlikely to dominate the total W source budget under the conditions considered and demonstrate the need for coupled modeling of RF waves, sheaths, sputtering, redeposition, and global impurity transport.

physics.plasm-ph↗

arXiv2026

Computing Reaction and Activation Energies of Pericyclic Reactions using a Symmetry-Adapted VQE Algorithm

Pericyclic reactions provide stringent tests for quantum simulations because their mechanisms are governed by orbital symmetry and involve correlated transition states. In this work, we employ the variational quantum eigensolver (VQE) combined with a previously established symmetry-guided active-space selection protocol based on symmetry-matched fractions (SMF-VQE) to simulate Diel-Alder and Alder-ene reactions in complex systems involving extended pi-conjugation and multiple bonding. Although absolute electronic energies obtained from the current protocol exhibit significant deviations from the values computed using CCSD method, the symmetry-guided active spaces yield substantial cancellation of deviations in the energy differences. As a result, reaction energies are predicted with error (relative to CCSD) less than one kcal per mol, while activation energies are reproduced within about five to six kcal per mol. The symmetry-guided protocol also reduces the large combinatorial space of active-space choices to a single symmetry-consistent selection for each reaction.

physics.chem-ph↗

arXiv2026

MPEX AI Digital Twins Milestone Report

This is the six month progress report to Fusion Energy Science (FES) and the American Science Cloud (AmSC) on the MPEX AI Digtial Twins project that was started in October 2025. There are two milestones to demonstrate the Artificial Intelligence (AI) advantage for MPEX operations and scientific discovery, that will be completed by June 2026. The first is a Helicon AI Hot-Spot Controller (Sec. 3.1), which is the helicon heating component of the more comprehensive planned MPEX AI Hot Spot Digital Twin (Sec. 3). The second is an E-beam Damage Assessment Digital Twin (Sec. 4.1), which is a reduced electron beam damage modality prototype for the MPEX AI Damage Assessment Digital Twin (Sec. 4). These two phase I milestones are on track for the June demonstration. In addition to these two milestones, progress on configuring the Galaxy software interface for automation, validation and data analysis is reported (Sec. 5). This interface now connects a subset of the main physics simulation codes to DOE HPC resources and will connect to the MPEX data acquisition system so that analysis of data, validation and execution of simulations can be performed by the scientist or by AI-Agents. When AmSC is ready to accept connections and data, Galaxy will be the MPEX interface to AmSC

physics.plasm-ph↗

arXiv2026

MPEX AI Digital Twins

Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project

physics.plasm-ph↗

arXiv2026

Vibrational strong coupling influences product selectivity in a model for post transition state bifurcation reactions

Understanding the mechanism of chemical reaction rate modulation by vibrational strong coupling (VSC) has been the focus of several recent studies. However, a definitive explanation for the mode-specificity of VSC still eludes us. In this study, we highlight the dynamics under VSC by utilizing a model for post-transition state bifurcation (PTSB) reactions coupled to an optical cavity. The minimal two-dimensional PTSB model features a valley-ridge inflection (VRI) point leading to bifurcated energetically asymmetric product wells. Here, we are interested in exploring whether the product selectivity (branching ratios) in such PTSB systems, known to be sensitive to dynamical effects, can be significantly perturbed under VSC conditions. Detailed classical and quantum dynamical calculations, along with systematic variation of the model parameters, reveals that the branching ratio can be enhanced under VSC by nearly a factor of two. Interestingly, for certain parameter regimes we find excellent classical-quantum correspondence. Apart from emphasizing the role of both cavity-system and intramolecular energy transfer in the observed enhancements, our study brings out the complexity of VSC in terms of the choice of the cavity frequency vis--\`a--vis the various molecular mode frequencies. In addition, our work highlights the potential of cavity quantum electrodynamics as a tool for reshaping dynamical outcomes in reactions with complex potential energy landscapes.

physics.chem-ph↗

arXiv2025

Heisenberg spin networks for realizing quantum battery with the aid of Dzyaloshinskii Moriya interaction

This work investigates the energy storage properties of quantum spin chains in the context of quantum batteries by introducing Heisenberg spin network models organized into different configurations, open, closed, supercube geometries, and c regular graphs. The charging dynamics of these systems are examined using Hamiltonians that include contributions from the battery, spin spin interactions, and a transverse magnetic field. Incorporating the Dzyaloshinskii Moriya interaction into the charging Hamiltonian is found to enhance the ergotropy in the XXZ model, particularly for the supercube configuration, thereby improving quantum battery performance. To explore the role of structural variations, we extend our study to c regular graphs with system sizes ranging from 3 to 12 qubits, including highly symmetric geometries such as the tetrahedron, octahedron, and icosahedron. These analyzes reveal that such symmetric structures retain ideal sinusoidal charging discharging behavior when DMI is tuned appropriately, establishing symmetry and coordination as key principles for scalable quantum battery architectures.

quant-ph↗

arXiv2025

Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research

The AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research. Fourteen interdisciplinary teams explored diverse nuclear science challenges using ChatGPT, Gemini, Claude, and other AI models over a single day. Applications ranged from developing foundation models for fusion reactor control to automating Monte Carlo simulations, predicting material degradation, and designing experimental programs for advanced reactors. Teams employed structured workflows combining prompt engineering, deep research capabilities, and iterative refinement to generate hypotheses, prototype code, and research strategies. Key findings demonstrate that LLMs excel at early-stage exploration, literature synthesis, and workflow design, successfully identifying research gaps and generating plausible experimental frameworks. However, significant limitations emerged, including difficulties with novel materials designs, advanced code generation for modeling and simulation, and domain-specific details requiring expert validation. The successful outcomes resulted from expert-driven prompt engineering and treating AI as a complementary tool rather than a replacement for physics-based methods. The workshop validated AI's potential to accelerate nuclear energy research through rapid iteration and cross-disciplinary synthesis while highlighting the need for curated nuclear-specific datasets, workflow automation, and specialized model development. These results provide a roadmap for integrating AI tools into nuclear science workflows, potentially reducing development cycles for safer, more efficient nuclear energy systems while maintaining rigorous scientific standards.

physics.comp-ph↗

arXiv2025

Domain specific ontologies from Linked Open Data (LOD)

Logical and probabilistic reasoning tasks that require a deeper knowledge of semantics are increasingly relying on general purpose ontologies such as Wikidata and DBpedia. However, tasks such as entity disambiguation and linking may benefit from domain specific knowledge graphs, which make it more efficient to consume the knowledge and easier to extend with proprietary content. We discuss our experience bootstrapping one such ontology for IT with a domain-agnostic pipeline, and extending it using domain-specific glossaries.

cs.IR↗

arXiv2025

Incremental Analysis of Legacy Applications Using Knowledge Graphs for Application Modernization

Industries such as banking, telecom, and airlines - o6en have large so6ware systems that are several decades old. Many of these systems are written in old programming languages such as COBOL, PL/1, Assembler, etc. In many cases, the documentation is not updated, and those who developed/designed these systems are no longer around. Understanding these systems for either modernization or even regular maintenance has been a challenge. An extensive application may have natural boundaries based on its code dependencies and architecture. There are also other logical boundaries in an enterprise setting driven by business functions, data domains, etc. Due to these complications, the system architects generally plan their modernization across these logical boundaries in parts, thereby adopting an incremental approach for the modernization journey of the entire system. In this work, we present a so6ware system analysis tool that allows a subject ma=er expert (SME) or system architect to analyze a large so6ware system incrementally. We analyze the source code and other artifacts (such as data schema) to create a knowledge graph using a customizable ontology/schema. Entities and relations in our ontology can be defined for any combination of programming languages and platforms. Using this knowledge graph, the analyst can then define logical boundaries around dependent Entities (e.g. Programs, Transactions, Database Tables etc.). Our tool then presents different views showcasing the dependencies from the newly defined boundary to/from the other logical groups of the system. This exercise is repeated interactively to 1) Identify the Entities and groupings of interest for a modernization task and 2) Understand how a change in one part of the system may affect the other parts. To validate the efficacy of our tool, we provide an initial study of our system on two client applications.

cs.SE↗

arXiv2025

Constructing Micro Knowledge Graphs from Technical Support Documents

Short technical support pages such as IBM Technotes are quite common in technical support domain. These pages can be very useful as the knowledge sources for technical support applications such as chatbots, search engines and question-answering (QA) systems. Information extracted from documents to drive technical support applications is often stored in the form of Knowledge Graph (KG). Building KGs from a large corpus of documents poses a challenge of granularity because a large number of entities and actions are present in each page. The KG becomes virtually unusable if all entities and actions from these pages are stored in the KG. Therefore, only key entities and actions from each page are extracted and stored in the KG. This approach however leads to loss of knowledge represented by entities and actions left out of the KG as they are no longer available to graph search and reasoning functions. We propose a set of techniques to create micro knowledge graph (micrograph) for each of such web pages. The micrograph stores all the entities and actions in a page and also takes advantage of the structure of the page to represent exactly in which part of that page these entities and actions appeared, and also how they relate to each other. These micrographs can be used as additional knowledge sources by technical support applications. We define schemas for representing semi-structured and plain text knowledge present in the technical support web pages. Solutions in technical support domain include procedures made of steps. We also propose a technique to extract procedures from these webpages and the schemas to represent them in the micrographs. We also discuss how technical support applications can take advantage of the micrographs.

cs.IR↗

arXiv2025

Automated Testing of COBOL to Java Transformation

Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterprise-level code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-based automatic transformation are encouraging, the resulting code cannot be trusted to correctly translate the original code, making manual validation of translated Java code from COBOL a necessary but time-consuming and labor-intensive process. In this paper, we share our experience of developing a testing framework for IBM Watsonx Code Assistant for Z (WCA4Z) [5], an industrial tool designed for COBOL to Java translation. The framework automates the process of testing the functional equivalence of the translated Java code against the original COBOL programs in an industry context. Our framework uses symbolic execution to generate unit tests for COBOL, mocking external calls and transforming them into JUnit tests to validate semantic equivalence with translated Java. The results not only help identify and repair any detected discrepancies but also provide feedback to improve the AI model.

cs.SE↗

arXiv2025

Automated Validation of COBOL to Java Transformation

Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterpriselevel code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-based automatic transformation are encouraging, the resulting code cannot be trusted to correctly translate the original code. We propose a framework and a tool to help validate the equivalence of COBOL and translated Java. The results can also help repair the code if there are some issues and provide feedback to the AI model to improve. We have developed a symbolic-execution-based test generation to automatically generate unit tests for the source COBOL programs which also mocks the external resource calls. We generate equivalent JUnit test cases with equivalent mocking as COBOL and run them to check semantic equivalence between original and translated programs.

cs.SE↗

arXiv2025

Quantum neural networks facilitating quantum state classification

The classification of quantum states into distinct classes poses a significant challenge. In this study, we address this problem using quantum neural networks in combination with a problem-inspired circuit and customised as well as predefined ans\"{a}tz. To facilitate the resource-efficient quantum state classification, we construct the dataset of quantum states using the proposed problem-inspired circuit. The problem-inspired circuit incorporates two-qubit parameterised unitary gates of varying entangling power, which is further integrated with the ans\"{a}tz, developing an entire quantum neural network. To demonstrate the capability of the selected ans\"{a}tz, we visualise the mitigated barren plateaus. The designed quantum neural network demonstrates the efficiency in binary and multi-class classification tasks. This work establishes a foundation for the classification of multi-qubit quantum states and offers the potential for generalisation to multi-qubit pure quantum states.

quant-ph↗

arXiv2025

Quantum Simulations of Chemical Reactions: Achieving Accuracy with NISQ Devices

Quantum computing is viewed as a promising technology because of its potential for polynomial growth in complexity, in contrast to the exponential growth observed in its classical counterparts. In the current Noisy Intermediate-Scale Quantum (NISQ) era, the Variational Quantum Eigensolver (VQE), a hybrid variational algorithm, is utilized to simulate molecules using qubits and calculate molecular properties. However, simulating a chemical reaction to compute the reaction energy using VQE algorithm has not yet reached chemical accuracy relative to the benchmark computational chemistry methods due to limitations such as the number of qubits, circuit depth, and noise introduced within the model. To address this issue, we propose the definition of different active spaces for studying chemical reactions, incorporating irreducible representations of both the ground and excited states of the molecules. Our results demonstrate that this approach achieves chemical accuracy in predicting the reaction energy for various reactions. For all reactions studied, the difference in reaction energies between conventional computational chemistry methods and the quantum-classical hybrid VQE algorithm is less than 1 kcal/mol. Furthermore, our analysis simplifies the process of selecting active spaces and electrons for each reaction, reducing it to a single optimal combination that ensures the chemical accuracy for each reaction.

physics.chem-ph↗

arXiv2024

CodeSAM: Source Code Representation Learning by Infusing Self-Attention with Multi-Code-View Graphs

Machine Learning (ML) for software engineering (SE) has gained prominence due to its ability to significantly enhance the performance of various SE applications. This progress is largely attributed to the development of generalizable source code representations that effectively capture the syntactic and semantic characteristics of code. In recent years, pre-trained transformer-based models, inspired by natural language processing (NLP), have shown remarkable success in SE tasks. However, source code contains structural and semantic properties embedded within its grammar, which can be extracted from structured code-views like the Abstract Syntax Tree (AST), Data-Flow Graph (DFG), and Control-Flow Graph (CFG). These code-views can complement NLP techniques, further improving SE tasks. Unfortunately, there are no flexible frameworks to infuse arbitrary code-views into existing transformer-based models effectively. Therefore, in this work, we propose CodeSAM, a novel scalable framework to infuse multiple code-views into transformer-based models by creating self-attention masks. We use CodeSAM to fine-tune a small language model (SLM) like CodeBERT on the downstream SE tasks of semantic code search, code clone detection, and program classification. Experimental results show that by using this technique, we improve downstream performance when compared to SLMs like GraphCodeBERT and CodeBERT on all three tasks by utilizing individual code-views or a combination of code-views during fine-tuning. We believe that these results are indicative that techniques like CodeSAM can help create compact yet performant code SLMs that fit in resource constrained settings.

cs.SE↗

arXiv2024

General Geospatial Inference with a Population Dynamics Foundation Model

Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations and researchers to understand and reason over complex relationships between human behavior and local contexts in order to identify high-risk groups and strategically allocate limited resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even, related tasks. To address this, we introduce a Population Dynamics Foundation Model (PDFM) that aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on all 27 geospatial interpolation tasks, and on 25 out of the 27 extrapolation and super-resolution tasks. We combined the PDFM with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers.

cs.LG↗

arXiv2024

Love Dynamical Model with persepectives of Piecewise Differential Operators

For love dynamical models, a new idea combining piecewise concept for integer-order, stochastic, and fractional derivatives is presented in order to capture the chaos and several crossover emotional scenerios. Under the assumptions of linear growth and Lipschitz condition, the fixed-point theorem explain the uniqueness and existence to the models under the investigation. The piecewise derivatives were approximated utilising the Lagrange interpolation method, and the computer results were demonstrated numerically for several values of order $\alpha$. It was observed that the recently presented new idea in love dynamical models can represent disordered emotional patterns in passionate loving partnerships.

math.GM↗

arXiv2024

Modified Lotka Volterra Model with Perspectives of the Piecewise Derivative

This study uses the Lotka Volterra Predator-Prey model to offer a notion of piecewise patterns for the various piecewise derivatives. Using the piecewise derivatives, we produced numerical solutions that are referred to as the Adams-Bashforth method. The computer results show piecewise patterns in the Lotka Volterra Predator-Prey model's real-world behaviours. The Lotka-Volterra model looks into the relationships between competition and abundance between two competing species. Changes in the abundance of one species are modelled as a function of the abundance of its competitors, but the competitive mechanism is given and evaluated. This notion led some scholars to label certain mathematical expressions as "phenomenological" and to propose a different theoretical framework that gives resources special consideration. The Lotka-Volterra model, often called the predator-prey model or the Lotka-Volterra model, is a nonlinear mathematical expression that is frequently used to analyse the dynamical behaviours of biological systems in which two species interact, one as a predator and the other as prey. The variation in the populations over time is illustrated by the mathematical statement.

math.GM↗