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DOE OSTI2020

Cross-facility science with the Superfacility Project at LBNL

As data sets from DOE user science facilities grow in both size and complexity there is an urgent need for new capabilities to transfer, analyze and manage the data underlying scientific discoveries. LBNL's Superfacility project brings together experimental and observational research instruments with computational and network facilities at the National Energy Research Scientific Computing Center (NERSC) and the Energy Sciences Network (ESnet) with the goal of enabling user science. Here, we report on recent innovations in the Superfacility project, including advanced data management, API-based automation, real-Time interactive user interfaces, and supported infrastructure for 'edge' services.

Enders, B↗
DOE OSTI2024

A Vision for the Science of Rare Isotopes

The field of nuclear science has considerably advanced since its beginning just over a century ago. Today, the science of rare isotopes is on the cusp of a new era with theoretical and computing advances complementing experimental capabilities at new facilities internationally. In this article we present a vision for the science of rare isotope beams (RIBs). We do not attempt to cover the full breadth of the field; rather, we provide a perspective and address a selection of topics that reflect our own interests and expertise. We focus in particular on systems near the drip lines, where one often finds nuclei that are referred to as exotic and where the role of the nuclear continuum is only just starting to be explored. An important aspect of this article is its attempt to highlight the crucial connections between nuclear structure and the nuclear reactions required to fully interpret and leverage the rich data to be collected in the next years at RIB facilities. Further, we connect the efforts in structure and reactions to key questions of nuclear astrophysics.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2019

ASCENDS: Advanced Data SCiENce Toolkit for Non-Data Scientists

Data science capabilities are becoming important more and more. Even though there exist many data science and machine learning tools, they are not intuitive for many traditional scientists who are not familiar with programming. ASCENDS provides easy and intuitive interfaces (command-line interface, graphical user interface) to users so they can use data science capabilities without having to understand programming language. ASCENDS performs data visualization, machine learning model training, storing and prediction (regression problem), automatic hyperparameter tuning, and supports various machine learning models including random forest, kernel ridge, nearest neighbor, neural network, etc. Users can use standard CSV (comma separated values) format data set and investigate the feature columns that are highly correlated to a target column; further, they can learn ML models, then store and use them for predictions. The tool is supposed to be generically applicable so that it can be used for many different applications.

Sangkeun, MattLee↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2021

2020 ETI Annual Summer School: Data Science and Engineering

The Consortium for Enabling Technologies & Innovation (ETI) was established in 2019 to address emerging technologies within the context of nuclear nonproliferation. ETI creates a research and education environment to support cross-cutting technologies across three core disciplines: 1) computer and engineering science research specifically in a form of machine learning and high performance computing (HPC), 2) advanced manufacturing, and 3) nuclear detection technologies. For outreach and development, ETI hosted the first of three summer schools from August 24-28, 2020 with the theme of “Data Science and Engineering”. The school was hosted in an on-line format and had over 200 participants. The recorded content is available on-line as a resource for students. The summer school had four modules: 1) Fundamentals of data Applications, 2) Computational Machine Learning, 3) Bayesian Modeling and Inference, and 4) Data Science for Safeguards. Modules contained both lectures as well as student exercises. Poll Everywhere was utilized in some modules as an on-line method to engage large groups of students. Upcoming ETI Summer Schools include Novel Instrumentation in 2021 and Advanced Manufacturing in 2022.

Biegalski, Steven R.↗
DOE OSTI2022

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse purposes covering a wide range of scientific topics. In order to be useful, they must offer realistically complex information about the galaxies they contain. Hence, it is critical to implement a rigorous validation procedure that ensures that the simulated galaxy properties faithfully capture observations and delivers an assessment of the level of realism attained by the catalog. We present here a suite of validation tests that have been developed by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). We discuss how the inclusion of each test is driven by the scientific targets for static ground-based dark energy science and by the availability of suitable validation data. The validation criteria that are used to assess the performance of a catalog are flexible and depend on the science goals. We illustrate the utility of this suite by showing examples for the validation of cosmoDC2, the extragalactic catalog recently released for the LSST DESC second Data Challenge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

NCSE's 13th National Conference on Disasters and Environment: Science, Preparedness and Resilience, Post Conference Follow-up Activities and Dissemination

The National Council for Science and the Environment (NCSE) received $15,000 from the US Department of Energy to support post-conference activities of the 13th National Conference on the theme of Disasters and the Environment: Science, Preparedness and Resilience, held on January 15-17, 2013 at the Ronald Reagan Building and International Trade Center in Washington, DC. Over 1,000 participants from the scientific, emergency response, policy, conservation, and business communities, as well as federal and local government officials, and international entities attended the event. The conference developed actionable outcomes that constructively advance the science behind decision-making on environmental disasters, with an intended result of more prepared and resilient communities in light of a changing climate. Disasters and Environment topic was addressed through six organizing themes: Cascading Disasters; Intersection of the Built and Natural Environments; Disasters as Mechanisms of Ecosystem Change; Rethinking Recovery and Expanding the Vision of Mitigation; Human Behavior and its Consequences; and "No Regrets" Resilience. The program featured eight plenary sessions, 24 symposia and 23 breakout workshops and addressed pivotal issues surrounding disasters and environment including lifeline services, the energy, climate, hazard nexus, grid collapse, community vulnerability, and natural resource management. Sessions, symposia and workshops were conducted by over 200 distinguished thought leaders, scientists, government officials, policy experts and international speakers throughout the three day event. Following the conference, NCSE prepared a set of recommendations and results from the workshops and disseminated the results to universities, organizations and agencies, the business community. NCSE’s national dissemination involved organized several targeted trips and meetings to disseminate significant findings to key stakeholder groups.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

Multiscale Computation: Needs and Opportunities for BER Science

The Environmental Molecular Sciences Laboratory (EMSL), a scientific user facility managed by Pacific Northwest National Laboratory for the U.S. Department of Energy, Office of Biological and Environmental Research (BER), conducted a one-day workshop on August 26, 2014 on the topic of “Multiscale Computation: Needs and Opportunities for BER Science.” Twenty invited participants, from various computational disciplines within the BER program research areas, were charged with the following objectives: Identify BER-relevant models and their potential cross-scale linkages that could be exploited to better connect molecular-scale research to BER research at larger scales, and Identify critical science directions that will motivate EMSL decisions regarding future computational (hardware and software) architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Development of Innovative Radioactive Isotope Production Techniques at the Pennsylvania State University Radiation Science and Engineering Center

The Penn State Breazeale Nuclear Reactor (PSBR) at the Radiation Science and Engineering Center (RSEC) has produced radioisotopes for research and commercial purposes since 1956. With the rebirth of the radiochemistry education and research program at the RSEC, the Center stands poised to produce a variety of radioisotopes for research and industrial work that is in line with the mission of the DOE Office of Science, Office of Nuclear Physics, Isotope Development and Production Research and Application Program. The RSEC received funding from the Office of Science in 2010 to improve production techniques and develop new capabilities. Under this program, we improved our existing techniques to provide four radioisotopes (Mn-56, Br-82, Na-24, and Ar-41) to researchers and industry in a safe and efficient manner. The RSEC is also working to develop new innovative techniques to provide isotopes in short supply to researchers and others in the scientific community, specifically Cu-64 and Cu-67. Improving our existing radioisotopes production techniques and investigating new and innovative methods are two of the main initiatives of the radiochemistry research program at the RSEC.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Harnessing Relativistic Laser Plasmas to Generate Intense Ion Beams: A Plasma Science Frontier White Paper

Optical lasers with high intensity (> 10 19 W/cm 2 ), high power (> 100 TW) and high energy (> 50 J) are enabling research that is expanding the frontiers of physics. Some of the research opportunities and importance are described in several reports. Here we highlight a prominent one: harnessing relativistic laser plasmas to generate intense ion beams, much more intense than it is possible with conventional particle-acceleration technology. This topic is important in two ways. On the one hand, it represents exceedingly exciting science in plasma physics and extreme electrodynamics (i.e., “high-field” physics). On the other hand, it enables advanced technology for advancing other fields of science, for societal applications (e.g., fast ignition8) and for national defense. There are tremendous opportunities for continued progress in existing laser facilities in the US (with laser intensities up to ~ 10 21 W/cm 2 , such as the Trident Laser Facility) provided modest funding support and reorientation of these lasers as national user facilities. Longer term, there are also great scientific opportunities for the US by extending our capabilities to intensities of ~ 10 22 W/cm 2 and beyond with shaped laser pulses of intermediate duration (~ 100 fs), to complement high intensity, shorter pulse length lasers planned worldwide. Such a complementary US facility would in fact excel in the ion-beam generation mission proposed here. At ~ 10 22 W/cm 2 and beyond, besides meeting the requirements for important societal applications of intense ion beams, novel high-field physics becomes important and amenable to study. That includes e.g., radiation self-force for electrons, and direct coupling of the laser to the ions (not just to electrons and then indirectly to the ions). These new physics in turn may open new technological opportunities, such as x-ray and gamma-ray-beam sources and new ion-acceleration mechanisms. It is hard to overstate the impact of particle accelerators starting in the last century to the present. Accelerators have had profound impact in society, both as a key enabling experimental tool for the modern scientific edifice, as well as for societal applications such as cancer therapy. Conventional accelerator technology is bumping against two main limitations. One is particle energy (at a reasonable size and cost) rooted in the ~ 25 MeV/m gradient limit from electrical breakdown of conventional accelerator structures. The second is beam intensity, limited by current and space-charge stability of conventional (non-neutral) particle beams. The former issue is being addressed in ingenious ways by “dielectric accelerators” which use high-power, low-energy lasers to set up transient-high fields in dielectric microstructures. These devices promise to advance the state of the art in acceleration gradients, but of course cannot address the intensity limitations. Ultimately, the highest performance is going to require a plasma as the acceleration medium. Plasmas can support the highest accelerating potential gradients and the highest currents. Moreover, by delivering neutralized beams (ions co-moving with unbound electrons), the usual current and space-charge limits can be circumvented. Realizing the highest ion energies and intensities requires a high-power density source to drive the system. Intense, high-power, high-energy lasers represent an ideal drive source, provided the physics can be understood and controlled, i.e., a challenge proposed here. To meet that challenge, the science community can build on the significant progress over the last decade, experimentally, computationally and theoretically.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Introducing Enabling Computational Tools to the Climate Sciences: Multi-Resolution Climate Modeling with Adaptive Cubed-Sphere Grids

The research investigates and advances strategies how to bridge the scale discrepancies between local, regional and global phenomena in climate models without the prohibitive computational costs of global cloud-resolving simulations. In particular, the research explores new frontiers in computational geoscience by introducing high-order Adaptive Mesh Refinement (AMR) techniques into climate research. AMR and statically-adapted variable-resolution approaches represent an emerging trend for atmospheric models and are likely to become the new norm in future-generation weather and climate models. The research advances the understanding of multi-scale interactions in the climate system and showcases a pathway how to model these interactions effectively with advanced computational tools, like the Chombo AMR library developed at the Lawrence Berkeley National Laboratory. The research is interdisciplinary and combines applied mathematics, scientific computing and the atmospheric sciences. In this research project, a hierarchy of high-order atmospheric models on cubed-sphere computational grids have been developed that serve as an algorithmic prototype for the finite-volume solution-adaptive Chombo-AMR approach. The foci of the investigations have lied on the characteristics of both static mesh adaptations and dynamically-adaptive grids that can capture flow fields of interest like tropical cyclones. Six research themes have been chosen. These are (1) the introduction of adaptive mesh refinement techniques into the climate sciences, (2) advanced algorithms for nonhydrostatic atmospheric dynamical cores, (3) an assessment of the interplay between resolved-scale dynamical motions and subgrid-scale physical parameterizations, (4) evaluation techniques for atmospheric model hierarchies, (5) the comparison of AMR refinement strategies and (6) tropical cyclone studies with a focus on multi-scale interactions and variable-resolution modeling. The results of this research project demonstrate significant advances in all six research areas. The major conclusions are that statically-adaptive variable-resolution modeling is currently becoming mature in the climate sciences, and that AMR holds outstanding promise for future-generation weather and climate models on high-performance computing architectures.

58 GEOSCIENCES↗
DOE OSTI2015

Advanced Instrumentation for Ultrafast Science at the LCLS

This grant supported a Single Investigator and Small Group Research (SISGR) application to enable multi-user research in Ultrafast Science using the Linac Coherent Light Source (LCLS), the world’s first hard x-ray free electron laser (FEL) which lased for the first time at 1.5 Å on April 20, 2009. The goal of our proposal was to enable a New Era of Science by requesting funds to purchase and build Advanced Instrumentation for Ultrafast Science (AIUS), to utilize the intense, short x-ray pulses produced by the LCLS. The proposed instrumentation will allow peer review selected users to probe the ultrasmall and capture the ultrafast. These tools will expand on the investment already made in the construction of the light source and its instrumentation in both the LCLS and LUSI projects. The AIUS will provide researchers in the AMO, Chemical, Biological and Condensed Matter communities with greater flexibility in defining their scientific agenda at the LCLS. The proposed instrumentation will complement and significantly augment the present AMO instrument (funded through the LCLS project) through detectors and capabilities not included in the initial suite of instrumentation at the facility. We have built all of the instrumentations and they have been utilized by scientists. Please see report attached.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2015

CILogon-HA. Higher Assurance Federated Identities for DOE Science

The CILogon-HA project extended the existing open source CILogon service (initially developed with funding from the National Science Foundation) to provide credentials at multiple levels of assurance to users of DOE facilities for collaborative science. CILogon translates mechanism and policy across higher education and grid trust federations, bridging from the InCommon identity federation (which federates university and DOE lab identities) to the Interoperable Global Trust Federation (which defines standards across the Worldwide LHC Computing Grid, the Open Science Grid, and other cyberinfrastructure). The CILogon-HA project expanded the CILogon service to support over 160 identity providers (including 6 DOE facilities) and 3 internationally accredited certification authorities. To provide continuity of operations upon the end of the CILogon-HA project period, project staff transitioned the CILogon service to operation by XSEDE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Livermore Accelerator Source for Radionuclide Science (LASRS)

The Livermore Accelerator Source for Radionuclide Science (LASRS) will generate intense photon and neutron beams to address important gaps in the study of radionuclide science that directly impact Stockpile Stewardship, Nuclear Forensics, and Nuclear Material Detection. The co-location of MeV-scale neutral and photon sources with radiochemical analytics provides a unique facility to meet current and future challenges in nuclear security and nuclear science.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Footprint of Sandia's August 15 2016 Informal Idea Exploration Session on "Towards an Engineering and Applied Science of Research".

On August 15, 2016, Sandia hosted a visit by Professor Venkatesh Narayanamurti. Prof Narayanamurti (Benjamin Peirce Research Professor of Technology and Public Policy at Harvard, Board Member of the Belfer Center for Science and International Affairs, former Dean of the School of Engineering and Applied Science at Harvard, former Dean of Engineering at UC Santa Barbara, and former Vice President of Division 1000 at Sandia). During the visit, a small, informal, all-day idea exploration session on "Towards an Engineering and Applied Science of Research" was conducted. This document is a brief synopsis or "footprint" of the presentations and discussions at this Idea Exploration Session. The intent of this document is to stimulate further discussion about pathways Sandia can take to improve its Research practices.

42 ENGINEERING↗
DOE OSTI2017

Complexity Science: A Mechanism for Strategic Foresight and Resiliency in National Security Decision-Making.

Most national policy decisions are complex with a variety of stakeholders, disparate interests and the potential for unintended consequences. While a number of analytical tools exist to help decision makers sort through the mountains of data and myriad of options, decision support teams are increasingly turning to complexity science for improved analysis and better insight into the potential impact of policy decisions. While complexity science has great potential, it has only proven useful in limited case s and when properly applied. In advance of more widespread use, a national - level effort to refine complexity science and more rigorously establish its technical underpinnings is recommended.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Large Scale Computing and Storage Requirements for Basic Energy Sciences: Target 2017. Report of the NERSC Requirements Review Conducted October 8–9, 2013, Gathersburg, MD

The National Energy Research Scientific Computing Center (NERSC) is the primary computing center for the DOE Office of Science, serving approximately 5,000 users working on some 700 projects that involve nearly 600 codes in a wide variety of scientific disciplines. In addition to large-scale computing and storage resources NERSC provides support and expertise that help scientists make efficient use of its systems. The latest review revealed several key requirements, in addition to achieving its goal of characterizing BES computing and storage needs. high-level findings are: 1) Scientists will need access to significantly more computational and storage resources to achieve their goalds and reach BES research objectives; 2) Users will need assistance from NERSC to prepare for Cori (NERSC-8) and follow-on manycore systems; 3) Research teams need to run complex jobs of many different types and scales; 4) BES is a leader in innovative use of HPC and requires a diverse set of resources and services from NERSC; and 5) BES facilities need computational analysis and data storage resources beyond what they can provide. This report expands upon these key points and add others. The results are based upon representative samples, called "case studies," of the needs of science teams within BES. The case study topics were selected by the NERSC meeting coordinators and BES program managers to represent the BES production computing workload. Prepared by BES workshop participants, the case studies contain a summary of science goals, methods of solution, current and future computing requirements, and special software and support needs. Also included are strategies for computing in the highly parallel "many-core" environment that is expected to dominate HPC architectures over the next few years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Toward a Compatible Reproducibility Taxonomy for Computational and Computing Sciences

Reproducibility is an essential ingredient of the scientific enterprise. The ability to reproduce results builds trust that we can rely on the results as foundations for future scientific exploration. Presently, the fields of computational and computing sciences provide two opposing definitions of reproducible and replicable. In computational sciences, reproducible research means authors provide all necessary data and computer codes to run analyses again, so others can re-obtain the results (J. Claerbout et al., 1992). The concept was adopted and extended by several communities, where it was distinguished from replication: collecting new data to address the same question, and arriving at consistent findings (Peng et al. 2006). The Association of Computing Machinery (ACM), representing computer science and industry professionals, recently established a reproducibility initiative, adopting essentially opposite definitions. The purpose of this report is to raise awareness of the opposite definitions and propose a path to a compatible taxonomy.

97 MATHEMATICS AND COMPUTING↗