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DOE OSTI2026

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↗

DOE OSTI2019

C version of mpibind

C version of mpibind, an algorithm to map MPI tasks, threads, and GPU kernels to the hardware.

LOUSSERT, ARTHUR TT↗

DOE OSTI2019

CICE Consortium Progress Report, June 2018 - December 2019

The CICE Consortium accomplished a number of major upgrades to our sea ice model code, scripts and testing infrastructure, while strengthening our visibility in the international modeling community. We released CICE versions 6.0.0, 6.0.1, 6.0.2 and 6.1.0, all of which include updated releases of Icepack. Major new capabilities include a floe size distribution, new coupling infrastructure, improvements to all of CICE’s dynamics options, a new atmospheric data set for historical simulations, overhauled documentation, and more robust and flexible scripts for running and testing the code. Several key testing processes were automated this year. In addition, the team is actively engaged in planning our first user workshop and tutorial in February 2020. In addition to overseeing modifications to the Consortium’s resources and code releases, management tasks included meeting with and updating the Consortium’s Sponsors group in October 2018, prioritizing tasks by mapping them onto potential release targets, and rescuing early CICE releases (version 1 through 3.14) from a disappearing LANL archive.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

A Modular Accelerator Robotics Framework for AD Robotics

Accelerator tunnels, such as the ones at Fermilab, remain highly radioactive after beam shutoff due to induced radiation from the beam. This residual radiation creates a hazardous environment for manual inspection and repair of beamline components. To minimize worker radiation dose and reduce beam downtime, the AD Robotics Initiative previously built a fleet of low-cost custom mobile robots. However, the custom Python sockets server-client architecture lacked standardization, causing development delays and complicating the integration of new sensors and actuators. Here, we developed a modular system using ROS2 and Docker to standardize the teleoperation and control interfaces. This system was validated by implementing a teleoperation controller with real-time, low-latency, and high-definition video feedback. The aim of this framework is for a new feature or even a robot to be integrated into the system simply by documenting the hardware configuration. Current integration of LiDAR, Odometry, and Depth Cameras provides the foundation for Simultaneous Localization and Mapping (SLAM) tasks. Finally, future work involves integration into the accelerator control system and the attachment of a 6 degree-of-freedom robotic arm for telemanipulation.

Rayyan Khan, M. [Fermilab; Rensselaer Poly.; Unlis↗

DOE OSTI2026

Modular Accelerator Robotics Framework Implementation For Accelerator Inspection

Rayyan Khana, M. [Unlisted, US, IL] (ORCID:0009000↗

DOE OSTI2025

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics

Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

On-the-fly closed-loop materials discovery via Bayesian active learning

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Feature Engineering and Process Mining to Enable Hazard Detection in Health Information Technology

Motivation for this work originates from a recognized need for developing data-driven approaches through analysis of complex electronic health records. Specifically, we aim to enhance the reliability of health information technology (HIT) systems by detection of plausible HIT hazards in clinical order transactions. In the absence of well-defined event logs in corporate data warehouses, our proposed approach identifies relevant timestamped data fields that could indicate transactions in the clinical order life cycle generating raw event sequences. Subsequently, we adopt state transitions of the OASIS Human Task standard to map the raw event sequences and simplify the complex process that clinical radiology orders go through. We describe how the current approach provides the potential to investigate areas of improvement and potential hazards in HIT systems. The discussion concludes with a use case and opportunities for future applications.

Ozmen, Ozgur↗

DOE OSTI2019

Separating the Wheat from the Chaff: Comparative Visual Cues for Transparent Diagnostics of Competing Models

Experts in data and physical sciences have to regularly grapple with the problem of competing models. Be it analytical or physics-based models, a cross-cutting challenge for experts is to reliably diagnose which model outcomes appropriately predict or simulate real-world phenomena. Expert judgment involves reconciling information across many, and often, conflicting criteria that describe the quality of model outcomes. In this paper, through a design study with climate scientists, we develop a deeper understanding of the problem and solution space of model diagnostics, resulting in the following contributions: i) a problem and task characterization using which we map experts’ model diagnostics goals to multi-way visual comparison tasks, ii) a design space of comparative visual cues for letting experts quickly understand the degree of disagreement among competing models and gauge the degree of stability of model outputs with respect to alternative criteria, and iii) design and evaluation of MyriadCues, an interactive visualization interface for exploring alternative hypotheses and insights about good and bad models by leveraging comparative visual cues. Here, we present case studies and subjective feedback by experts, which validate how MyriadCues enables more transparent model diagnostic mechanisms, as compared to the state of the art.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Method and apparatus for fast context cloning in a data processing system

A data processing system includes a memory system, a first processing element, a first address translator that maps virtual addresses to system addresses, a second address translator that maps system address to physical addresses, and a task management unit. A first program task uses a first virtual memory space that is mapped to a first system address range using a first table. The context of the first program task includes an address of the first table and is cloned by creating a second table indicative of a mapping from a second virtual address space to a second range of system addresses, where the second range is mapped to the same physical addresses as the first range until a write occurs, at which time memory is allocated and the mapping of the second range is updated. The cloned context includes an address of the second table.

Beard, Jonathan Curtis↗

DOE OSTI2018

Glovebox Handling of High-Consequence Materials with Super Baxter and Gesture-Based Programming - 18598

The handling of high-consequence materials is a difficult task requiring safe object manipulation while avoiding the risk of contamination or spillage. Specific operations including opening containers, segregating the wastes, and repackaging are required to be safely executed inside contained spaces such as gloveboxes. However, the workers' ability and dexterity to manipulate objects through thick protective gloves are compromised in many aspects. The fixed position of the glovebox arm ports restricts the movements of the workers' arms, which also makes it hard to lift heavy objects. Further, the operational workspace inside the glovebox is restricted by the gloves' reachability. Safety of workers is the paramount concern in glovebox operations and the very reason for their existence. Sharp edges and tools increases the risk of glove punctures, which may expose the operator to chemicals and radiation and risks contamination in the vicinity outside the glovebox. The operators are also affected by ergonomic stressors due to prolonged and repetitive operations. To achieve a high degree of human safety, robotic solutions to handle high-consequence materials inside the glovebox are desirable, as they remove the operators from the hazards listed above. However, robots, in general, lack the degree of adaptability necessary for most high-consequence material handling tasks as these tasks are often unstructured or highly variable. Likewise, most human operators, while highly skilled in the tasks at hand (task expertise), lack the robot programming skills necessary to adapt the robot's motions to the variations (programming expertise). A way to bridge this divide is to make robot programming more intuitive to human operators. Since humans naturally teach one another through demonstration and learning, robotic 'programming by demonstration' paradigms have begun to appear to reduce the burden of robot reprogramming. Gesture-Based Programming attempts to achieve that by enabling a robot to observe the normal actions and affordances of a human performing a task to learn to map those onto the skills of the robot. In the end, this approach permits benefits from both the human and the robot: the human's adaptive decision making and the robot's resilience to high-consequence material. Robotic solutions assuringly provide protection for the operators, however their autonomy brings about other potential risks. Artificial Intelligence is not perfect; it is a black box, which can produce results, but the full extent of those results may not be known precisely. In self-driving cars, a hypothetical AI problem is the 'green man detector'. Since self-driving cars have presumably never seen a green man, how can we know it won't veer toward him the first time it sees a green man? Likewise, even though GbP provides an open method for safe task execution replicating a human, incorrect task inferences can lead to potential collisions or unintended operations. Thus, a hardware safety measure is proposed to provide reliable operation inside the glovebox, and true safe operations are achieved by mechanically limiting the operational range of each arm link. The operational range is computed and validated through exhaustive offline simulations, which can be streamlined by affordable computing power. Super Baxter is one such robot which incorporates these capabilities. It is a human-like, bimanual robot, being developed at the Collaborative Robotics Lab (CRL) in conjunction with the Dept. of Energy/Environmental Management, Rethink Robotics, Barrett Technology, and the NSF Center for Robots and Sensors for the Human Well-Being (RoSe-HUB). Super Baxter is envisioned to represent the next generation of collaborative robots that will be intrinsically human-safe, as well as exceptionally human-intuitive. In summary, Super Baxter will enable safe glovebox operations through low-shot Gesture based Programming incorporated with hardware safety measures. Two aspects of safety will be discussed in the paper. First, the safety of the operator achieved by allowing him to program Super Baxter remotely through Gesture based Programming. Second, the safety of the glovebox, achieved by incorporating hardware safety measures using joint limits. Further, the capability of Super Baxter on Gesture based Programming is demonstrated through typical object manipulation tasks in glovebox operations. (authors)

42 ENGINEERING↗

DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗

DOE OSTI2025

A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling

Radiation mapping is a desirable task to automate because of the inherent risks involved and its tedious nature. A novel system was designed to address this by combining various existing technologies, utilizing behavior-based robotics and Bayesian optimization. The system uses a quadruped robot equipped with a manipulator and gamma detector to take measurements at locations that are selected based on the uncertainty of a surrogate model used to estimate the true radiation field. The robot uses input from the world with depth cameras to avoid collisions with the robot’s body, and unreachable points for the end effector are addressed by both allowing for a soft collision with the environment to occur, prompting the system to abandon that point, and varying the exploration tendency of the optimization based on consecutive collisions. This approach provides unique traversability and adaptability over other strategies in the literature. Experiments were performed by placing a Cesium-137 source on the ground and varying geometric setups and an optimization parameter demonstrating the adaptability to diverse environments and the increased robustness resulting from the designed behavior. The results additionally demonstrate that dynamically adjusting the optimization algorithm’s exploration tendency based on the arm’s collision history improves the system’s ability to navigate cluttered environments and construct accurate radiation maps without getting stuck in unreachable areas.

Adams, Joel↗

DOE OSTI2022

Adversarial Learning Based Discriminative Domain Adaptation for Geospatial Image Analysis

Not Available

58 GEOSCIENCES↗

DOE OSTI2018

Automatic identification and classification of Palomar Transient Factory astrophysical objects in GLADE

Palomar Transient Factory (PTF) is a comprehensive detection system for the identification and classification of transient astrophysical objects. In this paper, we make two significant contributions to the PTF pipeline. First, we present an experimental study that evaluates a novel implementation of the real-time classifier in GLADE - a parallel data processing system that combines the efficiency of a database with the extensibility of map-reduce. We show how each stage in the classifier maps optimally into GLADE tasks by taking advantage of the unique features of the system - range-based data partitioning, columnar storage, multi-query execution, and in-database support for complex aggregate computation. Second, we introduce a novel parallel similarity join algorithm for advanced transient classification. In conclusion, we implement this algorithm in GLADE and execute it on a massive supercomputer with more than 3,000 threads, achieving more than three orders of magnitude improvement over the PostgreSQL solution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Data Movement Categories

We have endeavored to classify the commonly seen data movement needs, as observed in data-intensive institutions (both commercial and non-profit), into four categories. Knowing how to map a data movement task into one of the four categories helps select proper data mover tools. For each category, how the data storage is involved, high-level examples and the nature of typical solutions are described. Finally, some general remarks are provided to help further orient readers new to this field - the 4th IT pillar.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Code Release for “Unlocking Extreme Space Weather through Advanced Modeling of Legacy Vela Spacecraft” ER

The software being developed for this project has two mains aims. First, a Bayesian Model Calibration (BMC) procedure is being developed to calibrate a spallation model that simulates protons hitting a spacecraft orbiting earth to real data. The procedure will be developed for general data (there is no data release requested as part of this code release). Second, an inverse physics modeling task is being undertaken to map the number of resulting neutrons observed from this process to the expected number of protons that hit the model. This second task is of statistical interest; to publish on it, the code will need to be open source.

Murph, Alexander↗