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This presentation outlines an existing UUR paper our team published (SAND2024-12532C) and will be presented at the Hawaii International Conference on System Sciences (HICSS) 58 conference on January 9, 2025.
We are honored to bring you this special issue dedicated to recent advances in computational mathematics and applications in science and engineering. The papers in this special issue were presented in ”Conference on Computational Mathematics and Applications (CCMA)” held at the University of Nevada Las Vegas (UNLV) during October 25–27, 2019. The conference has attracted about 90 attendees from six countries. The 15 papers in the special issue comprise a diverse collection of theoretical numerical analysis as well as applications in various subjects. Topics cover optimal control of PDEs, discontinuous Galerkin methods, fluid flow in porous media, turbulence flow, static and moving interface problems, complex fluids composed by the mixture of Newtonian fluid and nematic (liquid crystal) flows, modeling of ocean–atmosphere system, image segmentation algorithm, uncertainty quantification problems for turbulence model and Maxwell’s equations, iterative solver for systems resulting from mixed methods, and novel methods for solving systems of nonlinear equations. As the Guest Editors we would like to thank Dr. Leland Jameson at NSF (National Science Foundation) for kindly supporting our conference proposal which made this conference possible. We would also like to thank Darren Sugrue from Elsevier who provided partial support for our conference, and Thennarasu Gunasekaran and his production team from Elsevier who produced this nice special issue. We are grateful to many people (Dr. Zhijian Wu, Lori Ornelas, Elsa Juarez, and many Ph.D. students) at the Department of Mathematical Sciences of UNLV who provided tremendous support for the conference. Finally, we very much appreciate all authors who contributed their precious results to this special issue.
PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.
Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.
With the ever-increasing deluge of data and demand for warfighters to make decisions upon its analysis, U.S. defense strategy has prioritized the development of artificial intelligence (AI)/machine learning (ML) systems that can analyze multi-source data streams and suggest courses of action. However, there is a history of systems that have failed to be adopted by the warfighter due not only to unsolved technical challenges, but also a lack of usability or contributions to mission effectiveness, perceived or otherwise. To avoid this, research into human-AI teaming shows promise for developing AI systems that work with frontline operators. A recent National Academies of Sciences, Engineering, and Medicine report (NASEM, 2022) presented 57 research objectives in this area; however, the U.S. Navy requires a more-focused set of priorities, as it is impossible to tackle every priority. A workshop involving 23 human factors scientists, computer scientists, and active-duty sailors was organized at the Naval Information Warfare Center Pacific, resulting in a set of five research priorities spanning near-, mid-, and far-term time frames. This panel will summarize the results of this workshop, with a focus on the big questions both going into this workshop and coming out of it. The panel participants come from government, academia, and industry, providing perspective from the different kinds of organizations required to accomplish these research goals.
Los Alamos National Laboratory (LANL) is responsible for providing solutions to challenging issues through multidisciplinary science, engineering, and technology that impact national security. As part of the Radioactive Air Emissions Management (RAEM) Team, it is our responsibility to ensure LANL operates within the requirements of the Code of Federal Regulations 40 CFR 61, Subpart H, National Emission Standards for Emissions of Radionuclides Other Than Radon From Department of Energy Facilities (Rad-NESHAP). The standard states, “Emissions of radionuclides to the ambient air from Department of Energy facilities shall not exceed those amounts that would cause any member of the public to receive in any year an effective dose equivalent of 10 mrem/yr” (EPA, 2002) During a routine assessment of LANL’s Rad-NESHAP compliance program, an opportunity for improvement was identified that addresses the evaluation of routine stack samples which potentially have naturally occurring radioactive materials (NORM) on the filter media. In the past and based on professional judgment, detections of NORM such as K-40 and Be-7 typically were rejected from the analytical results. The assessment finding suggested developing a technical criterion to ensure that such rejections were justified, comparing actual measured values with anticipated NORM concentrations for various stacks located at LANL. The significance of accepting or rejecting samples as “true” or “NORM” and its impact on the RAEM’s mission and compliance to the regulations is explained later in the document. Additionally, this document will describe the process and calculations for how such results are to be accepted as actual samples “true positive” or rejected as NORM.
The ground truth program used simulations as test beds for social science research methods. The simulations had known ground truth and were capable of producing large amounts of data. This allowed research teams to run experiments and ask questions of these simulations similar to social scientists studying real-world systems, and enabled robust evaluation of their causal inference, prediction, and prescription capabilities. We tested three hypotheses about research effectiveness using data from the ground truth program, specifically looking at the influence of complexity, causal understanding, and data collection on performance. We found some evidence that system complexity and causal understanding influenced research performance, but no evidence that data availability contributed. The ground truth program may be the first robust coupling of simulation test beds with an experimental framework capable of teasing out factors that determine the success of social science research.
The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15-35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.
In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4‐year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio‐genomics‐based prognostic and predictive factors for HR+/HER− metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi‐omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as “maintaining effective communication” and “following good data science practices,” followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.
This report is a summary of our summer interns project that focused on investigating the usefulness of the Gamma Detector Response and Analysis Software (GADRAS) into the Material Control & Accountability (MC&A) Measurements Team. The project's findings highlight the advantages of using GADRAS and demonstrate its value in improving the MC&A Measurements Team's analytical capabilities.
This project focused on the development bimetallic metal-organic frameworks (MOFs) as gas phase heterogeneous catalysts for hydrogenation and hydroformylation reactions. MOFs are a new class of hybrid inorganic/organic materials that are highly crystalline, with structures consisting of metal nodes of specific geometries connected by organic linkers. Although there have been a number of studies of catalysis at MOF nodes in solution, there is little experimental data in the literature for gas phase reactions despite the fact that industrial heterogeneous catalysis on MOFs is more economically viable than homogeneous catalysis. The use of MOFs as heterogeneous catalysts presents the unique opportunity to carefully control the composition, geometry and ensemble sizes of the active sites, which are all critical factors for the rational design of new catalysts. Specific objectives of the project are as follows: (1) to tailor the geometry, composition and ensemble size of the active sites; (2) to understand how the adsorption of molecules at metal sites can be modified by interactions with a neighboring metal site; (3) to determine how oxidation states of the metal change during reaction and how these states can be modified by metal-metal electronic interactions; and (4) to elucidate reaction mechanisms and intermediates. For these studies, we have chosen to investigate selective hydrogenation of hydrocarbons and hydroformylation, which are industrially relevant reactions. Our interdisciplinary team of Chen, Shustova and Vogiatzis/Henkelman provides critical expertise in novel MOF synthesis (Shustova), atomic-scale surface science and catalysis (Chen) and computational studies of reaction mechanisms (Vogiatzis/Henkelman) that are necessary for the development of these catalysts. We believe that this work will lead directly to the rational design of new catalysts that are versatile, highly active/selective and suitable for industrial processes.
In support of the DOE Office of Science, Sandia National Labs is one of nine national laboratories which oversees the Atmospheric Radiation Measurement (ARM) Program. The Sandia ARM team has an obligation to fulfill its mission to provide the nation with data to improve the understanding of climate processes and the representation of those processes in climate models. Sandia’s ARM team’s ability to provide detailed and accurate descriptions of the Earth’s atmosphere in diverse climate regimes assists the DOE in development of sustainable solutions to the nation’s energy and environmental challenges. Sandia Labs manages ARM atmospheric facilities along the North Slope of Alaska (NSA) at Utqiagvik as well as the Tethered Balloon System (TBS). Activities conducted at NSA and with the TBS aide in data collection for the ARM data archive. An overview of these activities for the month of December follows.
Problem / Opportunity Statement: Future high-impact scientific applications at synchrotron and Free-Electron Laser (FEL) X-ray sources require the development of processing and mounting methods for ultra-thin diamond crystals to meet the most challenging needs of pioneering research at next generation light source facilities. These methods should be capable of manufacturing crystal plates as thin as 10 micrometers over an area of a few mm2, and with a surface cut in different crystal orientations, such as the diamond {100}, {111}, {110} and {113} planes. Project Purpose and Approach: In Phase I, Great Lakes Crystal Technologies (GLCT) partnered with Stonybrook University (SUNY) to created thin diamond drumhead crystals. The purpose of the Phase I project is to transfer the drumhead membrane window etching technology from SUNY to GLCT. Testing of a device using the beamline at Stanford Linear Accelerator Laboratory was initially planned, however, a delay on the part of a third party service provided required us to get beamline testing done at Argonne National Laboratory. Phase I Taskwork: Testing of a high purity diamond plate thinning and etching process was performed. By SUNY and GLCT. Plates were sent to a third party service provider for thinning to ~100 microns, however, delays required that GLCT develop an in-house thinning process to complete the taskwork. GLCT successfully developed this in-house capability even though it was initially planned to be part of the Phase II Taskwork. Multiple practice and deliverable plates were provided to SUNY to be etched into the target drumhead devices. GLCT practiced and refined an in-house plate thinning process and produced five plates with thicknesses in the 60 – 150 micron range. Processing of the plates has begun and will continue in a no-cost extension period necessitated by the third party service provider delay. GLCT also procured and installed a reactive ion etching/inductively coupled plasma system. In addition, one of the thinned plates was measured at the Applied Photon Source at APL for a pre-processing assessment of the crystalline strain. After processing the plate into a drumhead and annealing, a post-processing assessment of the strain will be performed. Phase I Results: The initial data from GLCT’s plate thinning process development, SUNY’s drumhead etching processing, and characterization at APS/ANL demonstrate our teams capabilities to achieve the project goals. Commercial Applications & Benefits: The proposed technology will benefit advanced x-ray science performed at BNL and the 70+ additional advanced beamlines around the world. There are also a number of medical and homeland security applications which could benefit from the technology.
Establishing that Vera C. Rubin Observatory is a flagship dark matter experiment is an essential pathway toward understanding the physical nature of dark matter. In the past two decades, wide-field astronomical surveys and terrestrial laboratories have jointly created a phase transition in the ecosystem of dark matter models and probes. Going forward, any robust understanding of dark matter requires astronomical observations, which still provide the only empirical evidence for dark matter to date. We have a unique opportunity right now to create a dark matter experiment with Rubin Observatory Legacy Survey of Space and Time (LSST). This experiment will be a coordinated effort to perform dark matter research, and provide a large collaborative team of scientists with the necessary organizational and funding supports. This approach leverages existing investments in Rubin. Studies of dark matter with Rubin LSST will also guide the design of, and confirm the results from, other dark matter experiments. Supporting a collaborative team to carry out a dark matter experiment with Rubin LSST is the key to achieving the dark matter science goals that have already been identified as high priority by the high-energy physics and astronomy communities.
The project goal is to study adsorbent materials and adsorption conditions in order to develop recommendations to improve the current BWR off-gas treatment operations with respect to cost savings. ANL tasks included a literature survey on relevant information for the adsorption of radionuclides (e.g., Xe and Kr), including adsorption coefficients on various charcoals, the effect of radionuclide concentration and moisture, and alternative sorption materials; participate in meetings and teleconferences as needed; prepare reports and support efforts INL and other organization Team Members.
A National Institutes of Health (NIH) and U.S. Department of Energy (DOE) Office of Science virtual workshop on shared general topics was held in July of 2021 and reported on in this publication in January of 2023. Following the inaugural 2021 joint meeting representatives from the DOE Office of Science and NIH met to discuss organizing a second joint workshop that would concentrate on radiation detection to bring together teams from both agencies and their grantee populations to stimulate collaboration and efficiency. To meet this scientific mission within the NIH and DOE radiation detection space, the organizers assembled workshop sessions covering the state–of–the–art in cameras, detectors, and sensors for radiation external and internal (diagnostic and therapeutic) to human, data acquisition and electronics, image reconstruction and processing, and the application of artificial intelligence. NIH and DOE are committed to continuing the process of convening a joint workshop every 12–24 months. This Special Report recaps the findings of this second workshop. Beyond showing only the innovations and areas of success, important gaps in our knowledge were defined and presented. Finally, we summarize by defining four areas of greatest opportunity and need that emerged from the unique, dynamic dialogue the in–person workshop provided the attendees.
Abstract The Radiation Emergency Assistance Center/Training Site (REAC/TS) is one of the US Department of Energy (DOE)/National Nuclear Security Administration (NNSA) Nuclear Emergency Response Team (NEST) assets and has been responding to radiological incidents since 1976. REAC/TS is in the Oak Ridge Institute for Science and Education (ORISE). A critical part of the REAC/TS mission is to provide emergency response, advice, and consultation on injuries and illnesses caused from ionizing radiation. Fortunately, radiation injuries are not frequent, but when they occur, they are more likely to be cutaneous radiation injuries (CRI) or internal contamination. In this paper, we will review selected cases from the REAC/TS experience in order to illustrate cutaneous patterns of injury and treatment options.
Join open writing teams to collaborate on commentaries for a special collection describing approaches that embody synthesis, cross-disciplinary integration, and open science across geosciences.