Approximate Forest Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees
Explore the source record for details and available documents.
Computer science↗
SEARCH · Engineering Papers
Results for “spanning tree”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Graph-Theoretic Approaches to Quantifying Power System Resiliency
Although gaining growing importance, the subject of power system resiliency still lacks a commonly acknowledged metric. As a contribution to solving this complication, in this paper we leverage the concepts of spanning trees and Fiedler value from graph theory to propose two topology-based indices for quantifying the resiliency of power systems. The proposed indices require least information and may be applied to any other flow network, such as water or gas pipeline networks.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Enabling Real-Time Communication in Multi-Agent Systems: A Graph Neural Network Based Approach
Global connectivity enables effective coordination in Multi-Agent Systems (MAS). Solving these connection problems under hardware constraints is an NP-hard non-Euclidean Degree Constrained Minimum Spanning Tree (DCMST) problem. Prior MAS controllers coordinate team movement for task completion and collision avoidance; some considering Line-of-Sight (LOS) maintenance but prioritizing flexibility over guarantees. Evolutionary Algorithms (EA) have been shown to find good solutions for DCMST, but their performance degrades with larger populations required to support a large MAS. We present a method based on edge graph attention networks, trained offline to reduce online computation times. Empirical comparisons with greedy polynomial-time solvers and EA show that our method leverages latent graph information to consistently find constraint-satisfying solutions in less time.
connectivity maintenance↗
ArborX 2.0
ArborX library tackles a problem of efficiently finding geometric objects that are close in space. Variations of this problem, such as finding the nearest neighbors of a point, or finding all objects within a certain distance, are inherent components of applications in many fields. The data may be large so that solving the problem efficiently may require significant computational resources, such as multiple processors or accelerators such as general purpose GPUs. ArborX' main advantage in its ability to solve large problems efficiently utilizing a combination of distributed and on-node parallelism. ArborX can be run efficiently on a wide variety of hardware, including GPUs from different vendors, which distinguishes it from other available libraries which typically choose only few of these. The other advantage is that it supports both types of user problems: spatial problems (useful for intersections and finding objects within certain distance), and nearest neighbor problems. ArborX also supports flexible interface in its interaction with a user. Particularly, it allows a user to call user's own function on a positive match, a functionality not rarely available in other libraries. ArborX implements construction and traversal algorithms using efficient tree structures, such as bounding volume hierarchy (BVH). At its core, ArborX uses linear BVH for its low construction cost and sufficient quality. ArborX implements both spatial and nearest-neighbor traversal algorithms. ArborX also provides several clustering algorithms (minimum spanning tree, DBSCAN, HDBSCAN*), interpolation using minimum least squares and ray tracing. ArborX is written using C++, and is parallelized using the message passing interface (MPI) for the distributed communication, and the Kokkos library for on-node parallelism. This approach allows ArborX to be run on a wide variety of hardware, from common laptops and desktops to supercomputers while using the same codebase.
Prokopenko, Andrey [Oak Ridge National Laboratory ↗
Efficient sparse state preparation via quantum walks
Continuous-time quantum walks (CTQWs) on dynamic graphs, referred to as dynamic CTQWs, are a recently introduced universal model of computation that offers a new paradigm in which to envision quantum algorithms. In this work, we develop an algorithm that converts single-edge and self-loop dynamic CTQWs to the gate model of computation. We use this mapping to introduce an efficient sparse quantum state preparation framework based on dynamic CTQWs. Our approach utilizes combinatorics techniques such as minimal hitting sets, minimum spanning trees, and shortest Hamiltonian paths to reduce the number of controlled gates required to prepare sparse states. We show that our framework encompasses the current state of the art ancilla-free sparse state preparation method by reformulating this method as a CTQW. This CTQW-based framework offers an alternative to the uniformly controlled rotation method used by Qiskit by requiring fewer CX gates when the target state has a polynomial number of non-zero amplitudes.
dynamic continuous time quantum walks↗
Clustering at Massive Scale
ClaMS provides hierarchical clustering technology for use on massive, high-dimensional datasets that require distributed memory for processing. The algorithm employed is inspired by the popular HDBSCAN algorithm but makes use of computational kernels better suited for distributed computing. ClaMS is built on scalable nearest neighbor graph construction, metric forest completion, and approximate minimum spanning tree techniques.
Stanley, ThomasA [Lawrence Livermore National Labo↗
FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)
This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.
36 MATERIALS SCIENCE↗
Tree root nutrient uptake kinetics vary with nutrient availability, environmental conditions, and root traits: a global analysis
Summary: Root nutrient uptake by trees is a critical process that couples carbon and nutrient cycling in forest ecosystems. Yet, root nutrient uptake traits are poorly constrained, and the dynamics of this process are often not represented in models reflecting sparse measurements and understanding of root nutrient uptake physiology that lags those of aboveground physiology in forest ecosystems. Here, we present a global dataset of published nutrient uptake capacity and affinity values for tree species, with the goal of describing global patterns and evaluating responses to environmental drivers and associations with root traits. Furthermore, the dataset contains observations for ammonium, nitrate, and phosphate uptake spanning 77 tree species. Nutrient uptake capacity and affinity varied by more than an order of magnitude for each nutrient. Notably, tropical forests are underrepresented in these observations. Nutrient uptake capacity was generally diminished under nutrient enrichment but enhanced with soil warming and root–mycorrhizal colonization. The magnitude and direction of these effects can depend on the duration of exposure to a given treatment. Species with thinner roots had a tendency toward greater uptake capacity and affinity. Overall, root nutrient uptake traits are highly variable across tree species, yet they depend on environmental drivers and life-history strategies.
Km↗
Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry
Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.
42 ENGINEERING↗
An accumulation method for early fault warning and its application to wind turbine systems
Unexpected failures in engineering systems lead to expensive maintenance actions and should be avoided if at all possible. This is particularly true for wind turbine systems for which unexpected failures not only demand costly repairs but also cause long downtime. Motivated by this need, we present an accumulation method for fault early warning and failure anticipation. Here, our research shows that one critical element allowing the ability of early warning is to accumulate the small-magnitude symptoms resulting from gradual changes in an engineering system like wind turbines. Our idea is inspired by the classical cumulative sum method, or CUSUM, but we have to redesign the accumulation mechanism for tackling unique challenges in wind turbine data. The new accumulation method is applied to two real wind turbine datasets, one with gearbox failures and the other with generator failures, and demonstrates superior performance as compared with CUSUM.
17 WIND ENERGY↗
From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure
Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.
channel finding↗
AstraAI v1
AstraAI is an open-source, structure-aware AI coding agent designed for large scientific and DOE-HPC codebases such as AMReX-based applications. Unlike general-purpose coding assistants, AstraAI combines retrieval-augmented generation (RAG) with compiler-level Abstract Syntax Tree (AST) analysis to perform precise, scope-constrained code modifications. It identifies exact function spans, enforces locality of edits, and maintains cross-file invariants, enabling deterministic and build-safe transformations in complex C++/GPU environments. AstraAI is intended for developers working on large, evolving HPC frameworks where correctness, reproducibility, and structural integrity are critical. Typical use cases include modifying physics kernels, updating GPU device lambdas, and performing multi-file refactors without breaking compilation or runtime semantics. Compared to conventional LLM-based coding agents - even those with repository access - AstraAI provides structural guarantees rather than free-form text patches. It minimizes unintended diffs, prevents scope drift, preserves formatting and build stability, and reduces structural hallucinations. By integrating compiler tooling directly into the generation loop, AstraAI transforms AI-assisted coding from probabilistic text editing into deterministic, structure-preserving program transformation suitable for mission-critical scientific software.
Natarajan, Mahesh [Lawrence Berkeley National Labo↗
Cosmological Hydrodynamics at Exascale: A Trillion-Particle Leap in Capability
Resolving the most fundamental questions in cosmology requires simulations that match the scale, fidelity, and physical complexity demanded by next-generation sky surveys. To achieve the realism needed for this critical scientific partnership, detailed gas dynamics must be treated self-consistently with gravity for end-to-end modeling of structure formation. Exascale computing enables simulations that span survey-scale volumes while incorporating key astrophysical processes that shape complex cosmic structures. We present results from CRK-HACC, a cosmological hydrodynamics code built for extreme scalability. Using separation-of-scale techniques, GPU-resident tree solvers, in situ analysis pipelines, and multi-tiered I/O, CRK-HACCexecuted Frontier-E: a four trillion particle full-sky simulation, over an order of magnitude larger than previous efforts. The run achieved 513.1 PFLOPs peak performance, processing 46.6 billion particles per second and writing more than 100 PB of data in just over one week of runtime. Frontier-E marks a significant advance in predictive modeling for next-generation cosmological science.
Frontiere, Nicholas [Argonne National Laboratory (↗
Response of soil nutrient pools and microbiomes to recurrent wildfire disturbance and varying burn severities in a mixed conifer forest
Wildfire is a pervasive disturbance in mixed-conifer forests, yet the relative influence of fire recurrence versus burn severity on soil biogeochemistry and microbial communities remains poorly quantified. We examined a natural gradient of fire history (0–3 prior fires) and burn severity (low–high) spanning 50 yr in a mixed-conifer ecosystem to assess how repeated fire shapes soil carbon (C) and nitrogen (N) pools, their isotopic signatures, mineral and particulate fractions, microbial community composition, carbon-use, CO₂ fluxes, and vegetation cover. Successive fires produced progressively higher bare-ground percentages and lower tree cover, which were tightly linked to declines in microbial diversity and reductions bulk %C, and %N. δ 13 C increased with fire frequency, indicating preferential loss of labile C through combustion or enhanced microbial oxidation, thereby explaining the observed net soil-C decline. Conversely, δ 15 N decreased and pH increased as tree density declined, reflecting altered N cycling and reduced acidification in post-fire soils. Fire recurrence, more than severity, corresponded with a marked shift in the bacterial community: for example, Xanthobacteraceae—key N-fixers and C-cyclers—diminished, while N-fixing Bacillaceae increased, underscoring the tightly coupled nature of soil nutrient dynamics and microbiome composition after repeated burns. Our results demonstrate that fire recurrence appears to be a stronger driver of post-fire soil ecosystem responses in this mixed-conifer forest, influencing both abiotic nutrient pools and the functional potential of the soil microbiome. These findings provide a more enhanced assessment and understanding to date of the biogeochemical consequences of repeated wildfire disturbance that can be used to inform management strategies aimed at preserving soil health in fire-prone landscapes.
54 ENVIRONMENTAL SCIENCES↗
Forest residue harvest optimization: spanning the bridge between plant biology and biorefinery performance
Forestry residues have immense potential as alternative feedstocks to petroleum, yet their inherent complexity remains a major challenge to widespread use. Pairing the temporal rhythms of plant biology with biorefinery performance is critical to industrial-scale biorefinery development. Here, we provide the first report of a techno-economic analysis (TEA) and life cycle assessment (LCA) for a model integrated reductive catalytic fractionation (RCF)–molten salt hydrolysis process for forestry residues varying in tree part, species, and phenophase. All forestry residues resulted in net-negative greenhouse gas (GHG) emissions vs. comparable petroleum feedstocks, with GHG emissions potentially reduced >4.0× through composition-based feedstock selection (e.g., harvesting American beech bark in spring vs. summer). Moreover, American beech twigs/branchlets and bark in leafed and emergence phenophases, respectively, had 7.9× lower predicted phenolic minimum selling prices (MSPs) vs. other feedstocks and MSPs within the current global phenolic market range. Hemicellulose content and RCF yield emerged as key parameters impacting GHG emissions and biorefinery revenue, identifying hardwood twigs/branchlets in the leafed phenophase as optimal biofeedstocks. Biorefinery expenses were dominated by purchased equipment, raw materials, and utility costs, highlighting essential areas for future study. Notably, RCF reactor pressures drove 85–90% of equipment costs, but sensitivity analysis revealed that decreasing the pressure 20% could reduce the phenol MSP 4-fold. Structural carbohydrate dynamics were also investigated using a two-step acid hydrolysis method to resolve tissue- and species-level patterns in biomass composition throughout the year to enable harvest optimization based on TEA/LCA findings. Ultimately, elucidating the impact of biofeedstock dynamics on biorefinery performance enables harvest optimization, informed engineering design, and progress towards an expanded bioeconomy.
Shapiro, Alison J. [University of Delaware, Newark↗
Simultaneous prediction of structural properties in epitaxially–grown GaN with quantum and conventional multi–output learning algorithms
Hundreds of GaN thin film crystal plasma–assisted molecular beam epitaxy synthesis experiment records spanning two decades were organized into a dataset correlating the growth experiment design parameters with discrete, binary determinations of crystallinity and surface morphology. Conventional data science techniques as well as both quantum and classical multi–output supervised machine learning algorithms were implemented to investigate the relationships between the operating parameter data and the structural figures of merit. Correlation coefficients, decision tree nodes, p–values, and SHAP values all support substrate temperature and gallium effusion cell conditions as being statistically significant for simultaneously influencing GaN crystallinity and surface morphology. Here, a conventional deep neural network learned best from the data, followed by a quantum–classical hybrid gradient boosting algorithm. When combined with calculations of uncertainty intervals based on VennAbers predictors, machine learning predictions of both structural properties show good agreement with results reported in published experimental literature.
36 MATERIALS SCIENCE↗
Estimators and Fusers for Fiber Delay Estimation Using Environmental Measurements
The properties of deployed network fiber are affected by environmental factors due to their exposure to the elements. Particularly for quantum networks, the resultant delay variations may have significant impacts due to the extreme sensitivity of synchronization, coincidence counting, and other critical operations. In this paper, the delays of 15 km aerial-inground fiber connections are measured, and effects due to temperature, humidity and wind speed are analyzed over multiple periods spanning four seasons of a year. Machine learning methods are first utilized to reveal surprisingly pronounced effects of humidity on the delay, in addition to the expected temperature and its seasonal variations. Estimator and fusion methods are developed to estimate the delay using temperature, humidity and wind speed measurements, by utilizing smooth Gaussian Process Regression (GPR) and nonsmooth Ensemble of Trees (EOT) methods. Measurements from winter and summer periods are temporally fused using twelve different methods, and eight methods provide estimates for the delay throughout the year with median test errors under 1.28%. The results reveal distinct temperature-humidity trends across the seasons, and the ability of estimator and temporal fusion methods to exploit them for estimating the delay. These results constitute a case study of machine learning analytical results, wherein generalization equations explain the performance of various estimator and fuser methods.
Rao, Nageswara [ORNL] (ORCID:0000000234085941)↗
Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints
Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.
43 PARTICLE ACCELERATORS↗