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  1. Funding for the 2ND IAEA technical meeting on fusion data processing, validation and analysis

    The International Atomic Energy Agency (IAEA) will organize the second Technical Meeting on Fusion Da Processing, Validation and Analysis from 30 May to 02 June, 2017, in Cambridge, MA USA. The meeting w be hosted by the MIT Plasma Science and Fusion Center (PSFC). The objective of the meeting is to provide a platform where a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolation needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucialmore » for a knowledge based understanding of the physical processes governing the dynamics of these plasmas. The meeting will aim at fostering, in particular, discussions of research and development results that set out or underline trends observed in the current major fusion confinement devices. General information on the IAEA, including its mission and organization, can be found at the IAEA websit Uncertainty quantification (UQ) Model selection, validation, and verification (V&V) Probability theory and statistical analysis Inverse problems & equilibrium reconstru ction Integrated data analysis Real time data analysis Machine learning Signal/image proc essing & pattern recognition Experimental design and synthetic diagnostics Data management« less
  2. Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing

    Scientific user facilities—particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more—operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity inmore » the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.« less
  3. Automated metadata, provenance cataloging and navigable interfaces: ensuring the usefulness of extreme-scale data

    The MPO (Metadata, Provenance, Ontology) Project successfully addressed the goal of improving the usefulness and traceability of scientific data by building a system that could capture and display all steps in the process of creating, analyzing and disseminating that data. Throughout history, scientists have generated handwritten logbooks to keep track of data, their hypotheses, assumptions, experimental setup, and computational processes as well as reflections on observations and issues encountered. Over the last several decades, with the growth of personal computers, handheld devices, and the World Wide Web, the handwritten logbook has begun to be replaced by electronic logbooks. This transitionmore » has brought increased capability such as supporting multi-media, hypertext, and fast searching. However, content creation and metadata (a set of data that describes and gives information about other data) capturing has for the most part remained a manual activity just as it was with handwritten logbooks. This has led to a fragmentation of data, processing, and annotation that has only accelerated as scientific workflows continue to increase in complexity. From a scientific perspective, it is very important to be able to understand the lineage of any piece of data: who, what, when, how, and why. This is typically referred to as data provenance. The fragmentation discussed previously often means that data provenance is lost. As scientific workflows move to powerful computers and become more complex, the ability to track all of the steps involved in creating a piece of data become even more difficult. It was the goal of the MPO (Metadata, Provenance, Ontology) Project to create a system (the MPO System) that allows for automatic provenance and metadata capturing in such a way to allow easy searching and browsing. This goal needed to be accomplished in a general way so that it may be used across a broad range of scientific domains, yet allow the addition of vocabulary (Ontology) that is domain specific as is required for intelligent searching and browsing in the scientific context. Through the creation and deployment of the MPO system, the goals of the project were achieved. An enhanced metadata, provenance, and ontology storage system was created. This was combined with innovative methodologies for navigating and exploring these data using a web browser for both experimental and simulation-based scientific research. In addition, a system to allow scientists to instrument their existing workflows for automatic metadata and provenance is part of the MPO system. In that way, a scientist can continue to use their existing methodology yet easily document their work. Workflows and data provenance can be displayed either graphically or in an electronic notebook format and support advanced search features including via ontology. The MPO system was successfully used in both Climate and Magnetic Fusion Energy Research. The software for the MPO system is located at https://github.com/MPO-Group/MPO and is open source distributed under the Revised BSD License. A demonstration site of the MPO system is open to the public and is available at https://mpo.psfc.mit.edu/. A Docker container release of the command line client is available for public download using the command docker pull jcwright/mpo-cli at https://hub.docker.com/r/jcwright/mpo-cli.« less
  4. Management, Analysis, and Visualization of Experimental and Observational Data -- The Convergence of Data and Computing

    Scientific user facilities---particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more---operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity inmore » the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.« less
  5. Plasma Simulation Program

    Many others in the fusion energy and advanced scientific computing communities participated in the development of this plan. The core planning team is grateful for their important contributions. This summary is meant as a quick overview the Fusion Simulation Program's (FSP's) purpose and intentions. There are several additional documents referenced within this one and all are supplemental or flow down from this Program Plan. The overall science goal of the DOE Office of Fusion Energy Sciences (FES) Fusion Simulation Program (FSP) is to develop predictive simulation capability for magnetically confined fusion plasmas at an unprecedented level of integration and fidelity.more » This will directly support and enable effective U.S. participation in International Thermonuclear Experimental Reactor (ITER) research and the overall mission of delivering practical fusion energy. The FSP will address a rich set of scientific issues together with experimental programs, producing validated integrated physics results. This is very well aligned with the mission of the ITER Organization to coordinate with its members the integrated modeling and control of fusion plasmas, including benchmarking and validation activities. [1]. Initial FSP research will focus on two critical Integrated Science Application (ISA) areas: ISA1, the plasma edge; and ISA2, whole device modeling (WDM) including disruption avoidance. The first of these problems involves the narrow plasma boundary layer and its complex interactions with the plasma core and the surrounding material wall. The second requires development of a computationally tractable, but comprehensive model that describes all equilibrium and dynamic processes at a sufficient level of detail to provide useful prediction of the temporal evolution of fusion plasma experiments. The initial driver for the whole device model will be prediction and avoidance of discharge-terminating disruptions, especially at high performance, which are a critical impediment to successful operation of machines like ITER. If disruptions prove unable to be avoided, their associated dynamics and effects will be addressed in the next phase of the FSP.« less
  6. Data catalog project—A browsable, searchable, metadata system

  7. Preface to the Special Issue: Strategic Opportunities for Fusion Energy

    Here, the Journal of Fusion Energy provides a forum for discussion of broader policy and planning issues that play a crucial role in energy fusion programs. In keeping with this purpose and in response to several recent strategic planning efforts worldwide, this Special Issue on Strategic Opportunities was launched with the goal to invite fusion scientists and engineers to record viewpoints of the scientific opportunities and policy issues that can drive continued advancements in fusion energy research.
  8. Fusion Energy Sciences Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Fusion Energy Sciences, January 27-29, 2016, Gaithersburg, Maryland

    The additional computing power offered by the planned exascale facilities could be transformational across the spectrum of plasma and fusion research — provided that the new architectures can be efficiently applied to our problem space. The collaboration that will be required to succeed should be viewed as an opportunity to identify and exploit cross-disciplinary synergies. To assess the opportunities and requirements as part of the development of an overall strategy for computing in the exascale era, the Exascale Requirements Review meeting of the Fusion Energy Sciences (FES) community was convened January 27–29, 2016, with participation from a broad range ofmore » fusion and plasma scientists, specialists in applied mathematics and computer science, and representatives from the U.S. Department of Energy (DOE) and its major computing facilities. This report is a summary of that meeting and the preparatory activities for it and includes a wealth of detail to support the findings. Technical opportunities, requirements, and challenges are detailed in this report (and in the recent report on the Workshop on Integrated Simulation). Science applications are described, along with mathematical and computational enabling technologies. Also see http://exascaleage.org/fes/ for more information.« less
  9. Verification and validation for magnetic fusion

    Dramatic progress in the scope and power of plasma simulations over the past decade has extended our understanding of these complex phenomena. However, as codes embody imperfect models for physical reality, a necessary step toward developing a predictive capability is demonstrating agreement, without bias, between simulations and experimental results. While comparisons between computer calculations and experimental data are common, there is a compelling need to make these comparisons more systematic and more quantitative. Tests of models are divided into two phases, usually called verification and validation. Verification is an essentially mathematical demonstration that a chosen physical model, rendered as amore » set of equations, has been accurately solved by a computer code. Validation is a physical process which attempts to ascertain the extent to which the model used by a code correctly represents reality within some domain of applicability, to some specified level of accuracy. This paper will cover principles and practices for verification and validation including lessons learned from related fields.« less
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"Greenwald, Martin"

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