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At least 703 records · Page 39

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DOE OSTI2025

Microtron Data Log

The Microtron at Los Alamos National Laboratory (LANL) is a versatile electron accelerator originally designed for medical therapy. Since 2001, it has been used for non-destructive radiographic imaging and research and development applications. Operating at four different energy levels—6, 10, 15, and 20 MeV—the Microtron produces dose rates of approximately 780, 1800, 2700, and 2800 R/min at a distance of one meter from the source, respectively. This high-energy X-ray source enables detailed internal examination of dense and thick objects without causing damage, making it invaluable for various scientific and industrial applications. For instance, LANL’s Microtron has been utilized to study the performance of large-panel cerium-doped lutetium yttrium silicon oxide (LYSO) scintillators, which are essential components in advanced imaging systems.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

symPACK: A GPU-Capable Fan-Out Sparse Cholesky Solver

Sparse symmetric positive definite systems of equations are ubiquitous in scientific workloads and applications. Parallel sparse Cholesky factorization is the method of choice for solving such linear systems. Therefore, the development of parallel sparse Cholesky codes that can efficiently run on today’s large-scale heterogeneous distributed-memory platforms is of vital importance. Modern supercomputers offer nodes that contain a mix of CPUs and GPUs. To fully utilize the computing power of these nodes, scientific codes must be adapted to offload expensive computations to GPUs. We present symPACK, a GPU-capable parallel sparse Cholesky solver that uses one-sided communication primitives and remote procedure calls provided by the UPC++ library. We also utilize the UPC++ "memory kinds" feature to enable efficient communication of GPU-resident data. We show that on a number of large problems, symPACK outperforms comparable state-of-the-art GPU-capable Cholesky factorization codes by up to 14x on the NERSC Perlmutter supercomputer.

Bellavita, Julian↗

NASA NTRS1991

The role of reliability graph models in assuring dependable operation of complex hardware/software systems

The complexity of computer systems currently being designed for critical applications in the scientific, commercial, and military arenas requires the development of new techniques for utilizing models of system behavior in order to assure 'ultra-dependability'. The complexity of these systems, such as Space Station Freedom and the Air Traffic Control System, stems from their highly integrated designs containing both hardware and software as critical components. Reliability graph models, such as fault trees and digraphs, are used frequently to model hardware systems. Their applicability for software systems has also been demonstrated for software safety analysis and the analysis of software fault tolerance. This paper discusses further uses of graph models in the design and implementation of fault management systems for safety critical applications.

Patterson-Hine, F. A.↗

NASA NTRS1989

Scientific work environments in the next decade

The applications of contemporary computer graphics to scientific visualization is described, with emphasis on the nonintuitive problems. A radically different approach is proposed which centers on the idea of the scientist being in the simulation display space rather than observing it on a screen. Interaction is performed with nonstandard input devices to preserve the feeling of being immersed in the three-dimensional display space. Construction of such a system could begin now with currently available technology.

Gomez, Julian E.↗

DOE OSTI2024

LLM integration into EPICS

The utilization of large language models (LLMs) such as ChatGPT has seen a remarkable increase in various fields over the past few years. These models have demonstrated their versatility and capability in understanding and generating human-like text, making them invaluable tools in numerous applications. In this project, we explore the integration of a LLM into the Experimental Physics and Industrial Control System (EPICS). The primary focus of this integration is to employ the LLM for advanced image processing and spatial analysis on images obtained from the beamlines. By leveraging the capabilities of the LLM, we aim to enhance the accuracy and efficiency of image interpretation, enabling more precise data analysis and decision-making within the EPICS framework. This integration not only showcases the potential of LLMs in scientific and industrial applications but also sets the stage for future advancements in automated control systems.

Adams, Ethan↗

NASA NTRS1994

A toolbox and record for scientific models

Computational science presents a host of challenges for the field of knowledge-based software design. Scientific computation models are difficult to construct. Models constructed by one scientist are easily misapplied by other scientists to problems for which they are not well-suited. Finally, models constructed by one scientist are difficult for others to modify or extend to handle new types of problems. Construction of scientific models actually involves much more than the mechanics of building a single computational model. In the course of developing a model, a scientist will often test a candidate model against experimental data or against a priori expectations. Test results often lead to revisions of the model and a consequent need for additional testing. During a single model development session, a scientist typically examines a whole series of alternative models, each using different simplifying assumptions or modeling techniques. A useful scientific software design tool must support these aspects of the model development process as well. In particular, it should propose and carry out tests of candidate models. It should analyze test results and identify models and parts of models that must be changed. It should determine what types of changes can potentially cure a given negative test result. It should organize candidate models, test data, and test results into a coherent record of the development process. Finally, it should exploit the development record for two purposes: (1) automatically determining the applicability of a scientific model to a given problem; (2) supporting revision of a scientific model to handle a new type of problem. Existing knowledge-based software design tools must be extended in order to provide these facilities.

Ellman, Thomas↗

DOE OSTI2025

Exascale workflow applications and middleware: An ExaWorks retrospective

Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and integrations are difficult to achieve due to the challenges of coordinating and deploying heterogeneous software components on diverse and massive platforms. Here, we present the ExaWorks project, which addresses many of these challenges. We developed a workflow Software Development Toolkit (SDK), a curated collection of workflow technologies that can be composed and interoperated through a common interface, engineered following current best practices, and specifically designed to work on HPC platforms. ExaWorks also developed PSI/J, a job management abstraction API, to simplify the construction of portable software components and applications that can be used over various HPC schedulers. The PSI/J API is a minimal interface for submitting and monitoring jobs and their execution state across multiple and commonly used HPC schedulers. We also describe several leading and innovative workflow examples of ExaWorks tools used on DOE leadership platforms. Furthermore, we discuss how our project is working with the workflow community, large computing facilities, and HPC platform vendors to address the requirements of workflows sustainably at the exascale.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Shift Happens: Building Robust AI Models with Domain Adaptation

Artificial Intelligence (AI) is revolutionizing physics research—from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data—exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This summer school session will introduce students to common challenges in applying AI across domains and present solutions based on domain adaptation—a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery. The concepts discussed are broadly applicable across physics and other scientific disciplines, making this a valuable topic for anyone interested in building robust, transferable AI models for science.

Ciprijanovic, A. [Fermilab] (ORCID:000000031281719↗

NASA NTRS2019

Establishing Calibration Standards for Remote Sensing Retrievals of Greenhouse Gases

While in situ greenhouse gas measurements have a concrete calibration standard, establishing a similar standard for remote sensing retrievals remains challenging. As the constellation of greenhouse gas observing satellites grows, such a standard is essential to ensure data are of the quality necessary to support scientific and policy applications. A primary goal of NASA's Atmospheric Carbon and Transport (ACT) - America aircraft campaign is to evaluate column CO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) through coordinated underflights. The campaign also includes airborne lidar instruments that measure the amount of CO2 and CH4 below the aircraft. This presentation introduces a system that establishes calibration standards for OCO-2 and lidar retrievals based on in situ data from the ACT-America campaign. The system assimilates the in situ data into NASA's Goddard Earth Observing System (GEOS) to produce high-resolution, two-dimensional transects of CO2 along the flight path which we refer to as curtains. Excluding the ability to sample the entire atmosphere at once, any such analysis must make assumptions about the connection of measurements at different places and times to a given retrieval. We chose to use the GEOS general circulation model forced by meteorology from its data assimilation system because their scientific merits are extensively documented. Furthermore, in areas rich in data, the assimilated curtains approach a field constrained by data alone. Where data are lacking, e.g., the stratosphere, age of air and other transport diagnostics can be used to quantify the uncertainty introduced by the model. Given these uncertainties, we can determine the uncertainties of the curtains and thus our ability to evaluate remote sensing instruments. Here, we demonstrate this for several flights over North America and discuss possible applications to upcoming missions, e.g., GeoCarb.

Weir, B.↗

NASA NTRS2020

A Roadmap for High-Resolution Satellite Soil Moisture Applications - Confronting Product Characteristics with User Requirements

Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. We discuss the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and/or through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of satellite-derived soil moisture are discussed, providing guidance for further development of operational soil moisture products and bridging the gap between the soil moisture user and supplier communities.

Jian Peng↗

NASA NTRS1985

Tethered Satellite System Project Overview

The Skyhook concept is reviewed and the use of a tethered satellite system (TSS) to enable scientific investigations from the shuttle using a closed loop control system is examined. The tethered satellite system has capabilities for deployment toward or away from Earth, for multiple round trip missions, and for deployment at distances up to 100 KN from the orbiter. The deployer, which consists of an entendable boom, a reel for the tether, and the tether itself, permits deployment and retrieval at a safe distance, allows alignment of the force vector of the tether through the center of gravity of the shuttle, and gives some initial gravity gradient separation to aid in deployment and ultimate retrieval of the tethered satellite. Charts show TSS activities in terms of systems studies, key guidelines, Italian and U.S. responsibilities, user activities, and major science and applications accommodation features. Scientific objectives for TSS-1 and TSS-2 verification missions and the current status of the project are also given.

Laue, J. H.↗

DOE OSTI2022

Observing Arctic Sea Ice

Our understanding of Arctic sea ice and its wide-ranging influence is deeply rooted in observation. Advancing technologies have profoundly improved our ability to observe Arctic sea ice, document its processes and properties, and describe atmosphere-ice-ocean interactions with unprecedented detail. Yet, our progress toward better understanding the Arctic sea ice system is mired by the stark disparities between observations that tend to be siloed by method, scientific discipline, and application. This article presents a review and philosophical design for observing sea ice and accelerating our understanding of the Arctic sea ice system. We give a brief history of Arctic sea ice observations and showcase the 2018 melt season within the context of five observational themes: spatial heterogeneity, temporal variability, cross-disciplinary science, scalability, and retrieval uncertainty. We synthesize buoy data, optical imagery, satellite retrievals, and airborne measurements to demonstrate how disparate data sets can be woven together to transcend observational-scale issues. The results show that there are limitations to interpreting any single data set alone. However, many of these limitations can be surmounted by combining observations that cross spatial and temporal scales. We conclude the article with pathways toward coordinating across observational platforms in order to: (1) optimize the scientific, operational, and community return on observational investments, and (2) facilitate a richer understanding of Arctic sea ice and its role in the climate system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Energy recovery proton linear accelerator

High-power proton linear accelerators have important applications in both scientific research and industry. However, the operation of such accelerators with megawatt-level beam power is expensive and limits the broad availability of these facilities. In this Letter, we propose a novel energy recovery proton linear accelerator in which the final GeV-level proton beam is reinjected into the linear accelerator from the accelerator exit and is decelerated in the same accelerator down to about 2 MeV, close to the initial beam energy. This substantially reduces the power consumption of the proton linear accelerator and also avoids the need for a high-power beam dump. We demonstrate this concept through self-consistent simulations and show that the beam-beam effect between the forward-accelerating beam and the backward-decelerating beam would not be a limiting factor for the proposed concept. A potential application of this concept to an electron-ion collider based on energy recovery electron and ion accelerators is discussed.

Energy recovery linacs↗

DOE OSTI2021

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗

DOE OSTI2019

GMLS-Nets: A Framework for Learning from Unstructured Data

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a non-parametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Pulse duration dependent effects of ultrafast laser induced damage on a 1030 nm multi-layer dielectric mirror for high repetition rate, high average power laser systems

High repetition rate, high peak, and average power laser systems are crucial for next-generation particle accelerators, inertial confinement fusion, and secondary particle sources. These applications demand durable laser optics, particularly interference coatings on optics lasting millions of shots at high fluence. This study focuses on designing, testing, and simulating multi-layer dielectric (MLD) mirrors for pulse durations of 260 fs, 77 fs, and 25 fs at 1030 nm wavelength and 45-degree incidence angle with p -polarization. S-on-1 laser-induced damage thresholds (LIDT) for varying pulse numbers were determined, with single-shot LIDT values of 0.98 Jcm -2 , 1.63 Jcm -2 , and 2.3 Jcm -2 for 25 fs, 77 fs, and 260 fs respectively. A strong correlation between blister shape and local fluence was observed, implying that the layer expansion in a blister depends on local fluence. We have also examined mechanisms responsible for laser-induced stress generation and energy release rates in blister formation. Damage mechanisms are further explored by finite-difference time-domain (FDTD) simulations, incorporating Keldysh strong field ionization, whose predictions were in excellent agreement with the onset of damage determined experimentally. These findings offer insights for enhancing MLD coating technology, promising more efficient and resilient laser systems for diverse scientific and industrial applications.

Noor, Mohamed Yaseen (ORCID:000000021036644X)↗

NASA NTRS2020

Virtual and Augmented Reality Applications in Science and Engineering

The articles in this special section focus on virtual and augmented reality applications in science and engineering. There has been an explosive growth in visually augmenting the spaces around us by creating environments where visual, aural, and kinesthetic immersive experiences afforded by virtual and augmented reality (AR) powerfully engage us in a way no other medium can. Virtual reality (VR) recreates the sensory world around us entirely through computer-generated signals of sight, sound, touch (and in some cases smell and taste). AR overlays the computer-generated sensory signals on the real world allowing the user to experience a rich juxtaposition of the virtual and the real worlds simultaneously. Together, these technologies are transforming the way people from all walks of life—scientists, engineers, educators, industrial workers, health care professionals, artists, and everyday people— see and use the information that matters most to them, in an intuitive embodied way. Just as mobile technology has revolutionized how we communicate with each other and with our digital worlds, ubiquitous VR and AR will fundamentally alter how our society creates, inspires, engages, and learns from the information-rich and enriched cyberspaces around us. While affordable consumer- quality VR and AR hardware is becoming available, significant work is needed to adopt VR and AR for important and difficult scientific and societal applications.

Virtual Reality, Augmented Reality↗

DOE OSTI2023

Integrated photonic molecule Brillouin laser with a high-power sub-100-mHz fundamental linewidth

Photonic integrated lasers with an ultra-low fundamental linewidth and a high output power are important for precision atomic and quantum applications, high-capacity communications, and fiber sensing, yet wafer-scale solutions have remained elusive. Here we report an integrated stimulated Brillouin laser (SBL), based on a photonic molecule coupled resonator design, that achieves a sub-100-mHz fundamental linewidth with greater than 10-mW output power in the C band, fabricated on a 200-mm silicon nitride (Si 3 N 4 ) CMOS-foundry compatible wafer-scale platform. The photonic molecule design is used to suppress the second-order Stokes (S2) emission, allowing the primary lasing mode to increase with the pump power without phase noise feedback from higher Stokes orders. The nested waveguide resonators have a 184 million intrinsic and 92 million loaded Q, over an order of magnitude improvement over prior photonic molecules, enabling precision resonance splitting of 198 MHz at the S2 frequency. We demonstrate S2-suppressed single-mode SBL with a minimum fundamental linewidth of 71±18 mHz, corresponding to a 23±6-mHz 2 /Hz white-frequency-noise floor, over an order of magnitude lower than prior integrated SBLs, with an ∼11-mW output power and 2.3-mW threshold power. The frequency noise reaches the resonator-intrinsic thermo-refractive noise from 2-kHz to 1-MHz offset. The laser phase noise reaches −155 dBc/Hz at 10-MHz offset. The performance of this chip-scale SBL shows promise not only to improve the reliability and reduce size and cost but also to enable new precision experiments that require the high-speed manipulation, control, and interrogation of atoms and qubits. Realization in the silicon nitride ultra-low loss platform is adaptable to a wide range of wavelengths from the visible to infrared and enables integration with other components for systems-on-chip solutions for a wide range of precision scientific and engineering applications including quantum sensing, gravitometers, atom interferometers, precision metrology, optical atomic clocks, and ultra-low noise microwave generation.

Liu, Kaikai (ORCID:0000000213609646)↗