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The accelerator complex now known as the Los Alamos Neutron Science Center (LANSCE) began operation in 1972 as LAMPF, the Los Alamos Meson Production Facility. In the intervening 53 years, the complex has been expanded in capability several times, both in terms of its accelerator performance and the user facilities it supports.
This dataset contains raw data (summary, chA, chB) from the ASSIST operated by NOAA Physical Science Laboratory on Block Island for WFIP3.
This dataset contains raw data (lv1) from the microwave radiometer (MWR) operated by NOAA Physical Science Laboratory on Nantucket Island for WFIP3.
This dataset contains raw data (summary, chA, chB) from the ASSIST operated by NOAA Physical Science Laboratory on Nantucket Island for WFIP3.
This dataset contains raw data (lv1) from the microwave radiometer (MWR) operated by NOAA Physical Science Laboratory on Block Island for WFIP3.
This dataset contains processed data from the ASSIST operated by NOAA Physical Science Laboratory on Block Island for WFIP3.
This dataset contains processed summary data from the ASSIST operated by NOAA Physical Science Laboratory on Nantucket Island for WFIP3.
This dataset contains processed data (lv1) from the microwave radiometer (MWR) operated by NOAA Physical Science Laboratory on Nantucket Island for WFIP3.
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the objective of this project is to develop science-based design and operation principles (as informed by TEA and LCA), which result in preprocessing units to operate predictably and reliably, and are capable of scalable performance.
In the study of feedstock variability, this presentation specifically looks at preprocessing processes that will created well-defined and homogenous feedstock from variable biomass resources, in the quest to produce homogeneous intermediates that will be converted into market-ready products. The objective of this stage of the project is to develop science-based design and operation principles informed by TEA/LCA that result in predictable, reliable, and scalable performance of preprocessing unit operations (comminution, fractionation, deconstruction, and real-time imaging).
A monthly summary of top achievements at Los Alamos National Laboratory for July 2021 in the areas of Nuclear Security; Science, Technology, and Engineering; Mission Operations; Community Relations, and Selected Media Coverage.
A monthly summary of top achievements for the month of September 2023 from Los Alamos National Laboratory, including Nuclear Security, Science, Technology and Engineering, Mission Operations, and Community Operations.
Brookhaven National Laboratory (BNL) is a multidisciplinary laboratory with seven Nobel Prize-winning discoveries, 37 R&D 100 Awards, and countless advances in science and technology. For over 75 years, the Laboratory has played a leading role in the Department of Energy (DOE) Science and Technology mission and continues to contribute to the DOE’s missions in energy resources, environmental quality, and national security. The Laboratory is operated and managed by Brookhaven Science Associates (BSA), which was founded by the Research Foundation for the State University of New York on behalf of Stony Brook University, and Battelle, a non-profit applied science and technology organization. BNL is committed to longstanding partnerships with researchers, academic institutions, industry, students, teachers, and the surrounding community. BNL is located near the center of Suffolk County on Long Island, New York within Brookhaven Township, about 60 miles east of New York City. Most of BNL’s principal facilities are located near the center of the 5,265-acre (8.23 square mile) site (Figure 1). The developed area encompasses approximately 1,820 acres, consisting of: ▪ 500 acres originally developed by the Army (as part of WW II Camp Upton) and still used for offices and other operational buildings. ▪ 200 acres occupied by large, specialized research facilities. ▪ 520 acres occupied by outlying facilities, such as the Sewage Treatment Plant, research agricultural fields, housing facilities, and fire breaks. ▪ 400 acres of roads, parking lots, and connecting areas. ▪ 200 acres developed for the Long Island Solar Farm. The balance of the site, approximately 3,400 acres, is largely wooded and represents a native pine barrens ecosystem. In November 2000, DOE set aside 530 acres of undeveloped land at BNL as the Upton Ecological and Research Reserve. The Upton Reserve preserves this portion of the pine barrens ecosystem and provides an area for ecological research and education activities.
Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.
Recognizing the dynamic environment and challenges facing the plutonium and actinide missions at Los Alamos National Laboratory over the next decade, Laboratory leadership established Fiscal Year (FY) 2020 Laboratory Agenda Item 2.6. This agenda item calls for the development and implementation of an integrated initiative for plutonium and actinide missions at Los Alamos. Over the next decade, Los Alamos has been asked to significantly ramp up pit production to help address nuclear stockpile and nuclear deterrence needs, heat source production to support expansion in space exploration, evaluation of stockpile returns, processing operations in support of nonproliferation, and the actinide science to support all of these efforts. All of this must be accomplished while significantly increasing the size of our plutonium and actinide missions workforce and modernizing and updating critical infrastructure. Only through enhanced integration of science, technology, and engineering (ST&E), manufacturing, and mission operations will we enable achievement of these critical missions. An overall summary of the plutonium and actinide missions is included in Figure 1 on Page 3 and Figure 2 on Page 4 summarizes our goals, objectives, FY 2021 recommended actions and internal strategic priorities that are key elements of our integrated initiative.
Abstract not provided.
This document outlines Rubin Observatory's plans for a dedicated \emph{Early Science Program} to enable high-impact science prior to the first annual data release of the Legacy Survey of Space and Time (LSST). Components of the Early Science Program include releasing science-grade commissioning data products via a series of ``Data Previews,'' ramping up of the transient alert stream during commissioning, implementing a program of incremental template generation to augment alert production in the early phases of the survey, and the first LSST Data Release, DR1, based on the first 6 months of data from the LSST. A detailed breakdown of which data products can be expected when is provided. The Rubin Operations team is working closely with the science community to optimize the Early Science Program for the time-domain and solar system science achievable in the first year of operations. This is a living document; both it and the Early Science Program will continue to evolve over the course of commissioning and pre-operations in response to the state of the as-built system and to community guidance.