NOODLES: Introducing Collaborative Visualization in a Flavorful Package
In this talk, we introduce NOODLES, describe it's design, use cases, and how to get started with the protocol.
collaborative analysis↗
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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.
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AI Enabled Co-Design of Polymer AM Targets
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11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
A computer program to solve the heat- conduction equation in the lunar surface for temperature-dependent thermal properties scientific report no. 7
Computer program to solve heat conduction equation in lunar surface for temperature-dependent thermal properties
HEAT TRANSFER↗
Position Papers for Inverse Methods for Complex Systems under Uncertainty Workshop
The ability to solve inverse problems – inferring unknown parameters, structures, or states of a system from observed data – is essential for advancing scientific discovery and innovation capabilities for the DOE mission. Basic research needs and challenges are particularly acute in emerging areas such as the interactive, data-driven, modeling and simulation of digital twins; decision support for experiments at DOE scientific user facilities; and for other complex systems and workflows. Inverse problems are at the heart of understanding and controlling complex systems due to factors such as observational data with varying modalities and fidelities, inherent uncertainties in physical measurements and numerical models, and the computational demands of rapid and high-fidelity simulations. The convergence of recent scientific computing trends – scientific machine learning, artificial intelligence, and computing advances such as exascale computing – is creating unprecedented opportunities. These advancements offer the potential to revolutionize how we approach inverse problems to extract actionable insights with the required level of accuracy and computational efficiency. This workshop and the Call for Position Papers are vital steps in bringing together experts to collectively explore and identify the new computational and mathematical directions needed in inverse methods for complex systems under uncertainty.
97 MATHEMATICS AND COMPUTING↗
High-Throughput Data Processing at FRIB Using ESnet
Real-time or nearly real-time (nearline) data processing methods are critical tools as detector technologies and data acquisition (DAQ) systems allow for higher data rates and volumes. The introduction of the energy sciences network (ESnet), a U.S. Department of Energy (DOE) supported high-speed network for scientific research, creates opportunities to leverage the computing power of DOE facilities like the National Energy Research Scientific Computing Center (NERSC). As a first step toward realizing a DOE Office of Science Integrated Research Infrastructure (IRI) pattern, an automated workflow was developed to remotely process data obtained from a nuclear physics experiment at the Facility for Rare Isotope Beams (FRIB) at NERSC with data transferred between FRIB and NERSC over ESnet. The workflow demonstrated the ability to process one week’s worth of experimental data in approximately 90 min and was used successfully for nearline analysis during a recently completed FRIB experiment. Here, a summary of the workflow development and results of recent demonstrations will be presented.
Data processing↗
High Energy Physics Network Requirements Review: Final Report, July 2024–December 2024
The world-class research infrastructure at the US Department of Energy (DOE) Office of Science (SC) provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance the core DOE mission in science and technology for its SC program to stimulate rich scientific discoveries and enhance its innovation ecosystem. Research communities gather and flourish around each user facility, bringing together new and enhanced perspectives. The continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by the SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high-performance data, networking, and computing solutions. The ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements review methodology. Between July and December 2024, the Energy Sciences Network (ESnet) and the Office of High Energy Physics (HEP) of the DOE-SC organized an ESnet requirements review of HEP-supported program activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about its relationship to the HEP program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Visualization of Computational Fluid Dynamics
Scientific visualization serves the dual purpose of exploration and exposition of the results of numerical simulations of fluid flow. Along with the basic visualization process which transforms source data into images, there are four additional components to a complete visualization system: Source Data Processing, User Interface and Control, Presentation, and Information Management. The requirements imposed by the desired mode of operation (i.e. real-time, interactive, or batch) and the source data have their effect on each of these visualization system components. The special requirements imposed by the wide variety and size of the source data provided by the numerical simulation of fluid flow presents an enormous challenge to the visualization system designer. We describe the visualization system components including specific visualization techniques and how the mode of operation and source data requirements effect the construction of computational fluid dynamics visualization systems.
Gerald-Yamasaki, Michael↗
Understanding Interactive and Reproducible Computing With Jupyter Tools at Facilities
Increasingly Jupyter tools are being adopted and incorporated into High Performance Computing (HPC) and scientific user facilities. Adopting Jupyter tools enables more interactive and reproducible computational work at facilities across data life cycles. As the volume, variety, and scope of data grow, scientists need to be able to analyze and share results in user friendly ways. Human-centered research highlights design challenges around computational notebooks, and our qualitative user study shifts focus to better characterize how Jupyter tools are being used in HPC and science user facilities today. We conducted twenty-nine interviews, and obtained 103 survey responses from NERSC Jupyter users, to better understand the increasing role of interactive computing tools in DOE sponsored scientific work. We examine a range of issues that emerge using and supporting Jupyter in HPC ecosystems, including: how Jupyter is being used by scientists in HPC and user facility ecosystems; how facilities are purposefully supporting Jupyter in their ecosystems; feedback NERSC users have about the facility’s deployment, and, discuss features NERSC indicated would be helpful. We offer a variety of takeaways for staff supporting Jupyter at facilities, Project Jupyter and related open source communities, and funding agencies supporting interactive computing work.
97 MATHEMATICS AND COMPUTING↗
Instrument requirements for imaging the magnetosphere in extreme-ultraviolet and energetic neutral atoms derived from computer-simulated images
Scientific requirements for He(+)(304 A) and energetic neutral atom (ENA) magnetospheric imaging, as well as the derived instrumental requirements are presented. Both ENA imaging of the hot plasma and EUV imaging of the cold plasma are highlighted. The question of the accuracy with which physically significant parameters can be extracted from actual images using computerized algorithms is addressed. An example of an ENA image analyzed by means of the Powell minimization algorithm is given. Automated unfolding of global magnetospheric images is also discussed. A Mercator projection of a model INA image is shown.
Roelof, Edmond C.↗
Improving NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization
NASA: The Research Access initiative is part of the agency's framework for increasing public access to scientific publications and digital scientific data. The initiative follows the release of White House Office of Science and Technology Policy's (OSTP) memorandum "Increasing Access to the Results of Federally Funded Research," to ensure federally funded research is available to the public within one year of publication. NASA answered the mandate by creating an agency plan entitled "NASA Plan for Increasing Access to the Results of Scientific Research" and associated policy, NPD 2230.1, Research Data and Publication Access. Principles in NASA SMD Strategic Plan for Scientific Data and Computing: Continued free and open access to scientific data for any use. Improved ease of use and discoverability. Enhanced science applications and new use cases. Incorporates best practices and "state of the art" through partnerships. Earth Data and Systems are Evolving: Increasing archive and file sizes. More complicated data structures. More user-friendly and data services. What is the future direction?
Liu, Zhong↗
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.↗
MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning
Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.
97 MATHEMATICS AND COMPUTING↗
Leveraging In-Network Computing and Programmable Switches for Streaming Analysis of Scientific Data
With the emergence of programmable network devices that match the performance of fixed function devices, several recent projects have explored in-network computing, where the processing that is traditionally done outside the network is offloaded to the network devices. In-network computing has typically been applied to network functions (e.g., load balancing, NAT, and DNS), caching, data reduction/aggregation, and coordination/consensus functions. In some cases it has been used to accelerate stream-processing tasks that involve small payloads and simple operations. In this work we focus on leveraging in-network computing for stream processing of scientific datasets with large payloads that require complex operations such as floating-point computations and logarithmic functions. We demonstrate in-network computing for a real-world scientific application performing streaming normalization of a 2-D image from a light source experiment. We discuss the challenges we encountered and potential approaches to address them.
Sankaran, Ganesh↗
Workshop on Manufacturing and Integration Challenges for Analog and Neuromorphic Computing
The U.S. Department of Energy’s (DOE’s) Advanced Manufacturing Office (AMO) held its third virtual workshop on Semiconductor R&D for Energy Efficiency on August 11-13, 2021. This public workshop – titled “Manufacturing and Integration Challenges for Analog and Neuromorphic Computing” – brought together more than 180 leading scientific and technical experts to identify challenges and opportunities to improve manufacturing capabilities for analog and neuromorphic computing architectures to significantly increase energy efficiency of microelectronic devices. The workshop featured perspectives of researchers from national laboratories, universities, government agencies, and industry with expertise in novel and emerging devices, processes, and metrology and characterization. This workshop is intended to inform an AMO research, development, demonstration, and deployment (RDD&D) plan to significantly reduce semiconductor energy use by 2030. It was co-sponsored by the Semiconductor Research Corporation (SRC) and the DOE Office of Science’s Advanced Scientific Computing Research (ASCR).
97 MATHEMATICS AND COMPUTING↗
Can the United States Maintain Its Leadership in High-Performance Computing? - A report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR Office
The United States (U.S.) is no longer the unambiguous leader in the vitally important field of high performance computing (HPC). Japan, the European Union (EU), and China have fielded systems that are on par with our fastest supercomputers. The supply chain for everything from semiconductors to scientific software is globally distributed. Yet our economic future and security depend critically on our ability to innovate faster than our competitors, and the speed of innovation depends increasingly on large-scale computational science and engineering and thus HPC. How should the United States respond to this challenge? This report seeks to initiate a new and potentially transformative national discussion on this vital question. The Department of Energy’s (DOE) Advanced Scientific Computing Research (ASCR) program is well-positioned to make informed, targeted decisions about where the United States should cooperate and where it should compete in the global market for scientific exploration and discovery. By setting its sights on problems critical to our nation and the world, by establishing productive new collaborations, and by making strategic investments, ASCR can restore and maintain U.S. scientific leadership in the critical areas described in this report while strengthening our research infrastructure and training a large, diverse cohort of scientists. In doing so, ASCR and its scientists will pave the way for a secure and prosperous future for America. For more than 30 years, the ASCR program has provided the HPC and networking capabilities and expertise needed to support DOE’s mission to advance the national, economic, and energy security of the United States. The program now faces the challenge of developing and deploying the next generation of HPC systems and technologies, as well as supporting the application of HPC and artificial intelligence (AI) technologies to a wide range of scientific and engineering research problems. Through its research and development efforts, the ASCR program must also advance the state of the art in HPC and accelerate the pace of scientific discovery and technological innovation. Fulfilling this promise will require significantly increased investments, as well as innovative policies and programs. This subcommittee is aware that we are making recommendations and calls for action at a time when federal resources are limited. We understand that a wide range of competing priorities must be balanced by the nation’s leaders and that there is a need to leverage resources in new ways and seek efficiencies in facilities and operations. However, we must not let these realities limit our imagination or silence our advocacy. The ASCR program is a key part of the U.S. research infrastructure and an important component of economic growth and U.S. competitiveness. ASCR has a responsibility to pursue its mission, including advanced scientific computing, applications of AI technologies, and the required advanced research facilities, with determination and enthusiasm. To fulfill the scientific enterprise’s responsibility to the nation, the ASCR program must not only develop and publish a clear vision with an associated list of goals, priorities, and recommendations but also demonstrate scientific leadership by consistently securing long-term funding. This will allow the program to build on its achievements to date, to realize its ambitious vision, and to make lasting contributions to the field.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Modeling performance of data collection systems for high-energy physics
Exponential increases in scientific experimental data are outpacing silicon technology progress, necessitating heterogeneous computing systems—particularly those utilizing machine learning (ML)—to meet future scientific computing demands. The growing importance and complexity of heterogeneous computing systems require systematic modeling to understand and predict the effective roles for ML. We present a model that addresses this need by framing the key aspects of data collection pipelines and constraints and combining them with the important vectors of technology that shape alternatives, computing metrics that allow complex alternatives to be compared. For instance, a data collection pipeline may be characterized by parameters such as sensor sampling rates and the overall relevancy of retrieved samples. Alternatives to this pipeline are enabled by development vectors including ML, parallelization, advancing CMOS, and neuromorphic computing. By calculating metrics for each alternative such as overall F1 score, power, hardware cost, and energy expended per relevant sample, our model allows alternative data collection systems to be rigorously compared. We apply this model to the Compact Muon Solenoid experiment and its planned high luminosity-large hadron collider upgrade, evaluating novel technologies for the data acquisition system (DAQ), including ML-based filtering and parallelized software. The results demonstrate that improvements to early DAQ stages significantly reduce resources required later, with a power reduction of 60% and increased relevant data retrieval per unit power (from 0.065 to 0.31 samples/kJ). However, we predict that further advances will be required in order to meet overall power and cost constraints for the DAQ.
Olin-Ammentorp, Wilkie (ORCID:0000000224729862)↗
Scientific mission support for extended lunar exploration, volume 5 - Appendix B - Master data report - Computer printouts Final report
Computer printouts of numerous scientific experiments conducted in mission support for extended lunar exploration
LUNAR EXPLORATION↗
Composable optimization and control toolkit for scientific applications
Applications of Artificial Intelligence (AI) and Machine Learning (ML) can improve the computational efficiency and scientific research output. In order to improve interoperability and reuse of AI/ML software, a composable approach is required. This talk presents a composable approach for scientific workflow development that allows seamless integration of various modules developed by independent researchers. These practices will reduce redundant software development by allowing re-use of workflow modules across projects, teams, departments and facilities. We will present three use cases that follow the composable approach namely, Scientific Optimization and Control Toolkit (SOCT), SciDAC QuantOm workflow, and JLab Nuclear Physics experimental workflows. This talk will dive deeper into SOCT and present the details of the composable code development for optimization and control algorithms using reinforcement learning.
Rajput, Kishansingh↗