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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

DuoModel: Leveraging Reduced Model for Data Reduction and Re-Computation on HPC Storage

High-performance computing (HPC) applications generate large amounts of floating-point data that need to be stored and analyzed efficiently to extract the insights and advance knowledge discovery. With the growing disparities between compute and I/O, optimizing the storage stack alone may not suffice to cure the I/O problem. There has been a strong push in the HPC communities to perform data reduction before data is transmitted to storage in order to lower the I/O cost. However, as of now, neither lossless nor lossy compressors can achieve the adequate reduction ratio that is desired by applications. This paper proposes DuoModel, a new approach that leverages the similarity between the full and reduced application models, and further improve the data reduction ratio. DouModel further improves the compression ratio of state-of-the-art compressors via compressing the differences (termed as delta) between the data products of the two models. For data analytics, the high fidelity data can be re-computed by launching the reduced model and applying the compressed delta. Our evaluations confirm that DuoModel can further push the limit of data reduction while the high fidelity of data is maintained.

Luo, Huizhang (ORCID:0000000323920267)↗
DOE OSTI2013

Ultrascale Visualization of Climate Data

To support interactive visualization and analysis of complex, large-scale climate data sets, UV-CDAT integrates a powerful set of scientific computing libraries and applications to foster more efficient knowledge discovery. Connected through a provenance framework, the UV-CDAT components can be loosely coupled for fast integration or tightly coupled for greater functionality and communication with other components. This framework addresses many challenges in the interactive visual analysis of distributed large-scale data for the climate community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2014

Feature-Based Analysis of Plasma-Based Particle Acceleration Data

Plasma-based particle accelerators can produce and sustain thousands of times stronger acceleration fields than conventional particle accelerators, providing a potential solution to the problem of the growing size and cost of conventional particle accelerators. To facilitate scientific knowledge discovery from the ever growing collections of accelerator simulation data generated by accelerator physicists to investigate next-generation plasma-based particle accelerator designs, we describe a novel approach for automatic detection and classification of particle beams and beam substructures due to temporal differences in the acceleration process, here called acceleration features. The automatic feature detection in combination with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features enables an effective top-down data exploration process, starting from a high-level, feature-based view down to the level of individual particles. We describe the application of our analysis in practice to analyze simulations of single pulse and dual and triple colliding pulse accelerator designs, and to study the formation and evolution of particle beams, to compare substructures of a beam and to investigate transverse particle loss.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

TU-C-HORIZONS-01: The Expanding Horizons Travel Grant Program: ePosters and Discussion

The Expanding Horizons travel grant program provides opportunity for students and trainees to broaden the scope of scientific meetings they attend and gain insight from research outside traditional domains of medical physics. Through participation in such conferences, early-career researchers are introduced to new topics with relevance to medical physics research as a means to expand the scientific horizons of our discipline. This year, 21 Expanding Horizons travel grants were awarded, granting travel to 17 conferences, including: Radiomics, the World Molecular Imaging Society (WMIS), the 3D Printing Conference and Expo, the GPU Technology Conference, the SIAM Imaging Science Conference, the Human Brain Mapping Conference, the OSA Conference on Clinical and Translational Biophotonics, the Society for Neuroscience, the AACR Conference on Tumor Microenvironment, and the Conference on Knowledge Discovery and Data Mining. The Expanding Horizons electronic poster session gives a venue for AAPM conference attendees to meet and discuss with awardees, learn the hot topics and emerging research areas presented at these conferences, and understand the relevance to future medical physics research.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2012

High-performance analysis of filtered semantic graphs

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry "attributes" of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must either view the graph through a filter that passes only those individual vertices and edges of interest, or else must first materialize a subgraph or subgraphs consisting of only the vertices and edges of interest. The filtered approach is superior due to its generality, ease of use, and memory efficiency, but may carry a performance cost. In the Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, the user writes filters in a high-level language, but those filters result in relatively low performance due to the bottleneck of having to call into the Python interpreter for each edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance C++ /MPI Combinatorial BLAS engine, and show that the productivity gained by using a high-level filtering language comes without sacrificing performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Big Data as a Service from an Urban Information System

Big Data has already proven itself as a valuable tool that lets geographers and urban researchers utilize large data re- sources to generate new insights. However, wider adoption of Big Data techniques in these areas is impeded by a number of difficulties in both knowledge discovery and data and sci- ence production. Typically users face such problems as dis- parate and scattered data, data management, spatial search- ing, insufficient computational capacity for data-driven anal- ysis and modelling, and the lack of tools to quickly visual- ize and summarize large data and analysis results. Here we propose an architecture for an Urban Information Sys- tem (UrbIS) that mitigates these problems by utilizing the Big Data as a Service (BDaaS) concept. With technolog- ical roots in High-performance Computing (HPC), BDaaS is based on the idea of outsourcing computations to dif- ferent computing paradigms, scalable to super-computers. UrbIS aims to incorporate federated metadata search, in- tegrated modeling and analysis, and geovisualization into a single seamless workflow. The system is under active devel- opment and is built around various emerging technologies that include hybrid and NoSQL databases, massively par- allel systems, GPGPU computing, and WebGL-based geo- graphic visualization. UrbIS is designed to facilitate using Big Data across multiple cities to better understand how ur- ban areas impact the environment and how climate change and other environmental change impact urban areas.

Sorokine, Alexandre↗
DOE OSTI2019

Using Digital Trace Data to Identify Regions and Cities

A greater understanding of human dynamics as they play out in both physical space and through inter-personal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight in to the space-time distribution of population and their activity patterns, while inter-personal communication can, for the first time, be measured at scale through virtual interactions. The confluence of these two enables spatial social network analysis in the built environment for edge computing, knowledge discovery, and artificial intelligence activities. In this work, we propose a novel method to discover such dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We find that the broad spatial delineation of communities and community sub-structure is consistent with major United States socio-cultural regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of socially cohesive regions that can be consistently applied anywhere there is a sufficiently rich data source. We hope the proposed methods will generate new perspectives on smart and resilient cities.

Brelsford, Christa↗
DOE OSTI2019

Scalable Causal Graph Learning through a Deep Neural Network

Learning the causal graph in a complex system is crucial for knowledge discovery and decision making, yet it remains a challenging problem because of the unknown nonlinear interaction among system components. Most of the existing methods either rely on predefined kernel or data distribution, or they focus simply on the causality between a single target and the remaining system. This work presents a deep neural network for scalable causal graph learning (SCGL) through low-rank approximation. The SCGL model can explore nonlinearity on both temporal and intervariable relationships without any predefined kernel or distribution assumptions. Through low-rank approximation, the noise influence is reduced, and better accuracy and high scalability are achieved. Experiments using synthetic and real-world datasets show that our SCGL algorithm outperforms existing state-of-the-art methods for causal graph learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fragile Earth: AI for Climate Mitigation, Adaptation, and Environmental Justice

The Fragile EarthWorkshop is a recurring event that gathers the research community to find and explore howdata science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs).Fragile Earth 2022: AI for Climate Mitigation, Adaptation, and Environmental Justice is a workshop taking place as part of the ACM's KDD 2022 Conference on research in knowledge discovery and data mining and their applications. The dates for the Conference are August 14-18, 2022.

Abe, Naoki↗
DOE OSTI2022

Essential Function Analysis Capability

The Essential Function Analysis Capability (EFAC) is an extension of the All Hazards Knowledge Framework (AHA) and is a dynamic analytical framework that enables critical infrastructure knowledge discovery and decision support across the five mission areas – prevention, protection, mitigation, response and recovery. EFAC provides the ability to store and model infrastructure systems as a linked multigraphs providing an intuitive and natural representation. These infrastructure systems can then be tied to the organizational and essential function breakdown of an entity. This capability provides the foundation to rapidly evaluate and understand the potential consequences of manmade and natural disaster on these infrastructure systems and essential functions.

Hoover, MichaelJ.↗
DOE OSTI2025

pnnl/HypoGen

An innovative workflow for automatic knowledge discovery in material science, combining experimental automation with advanced machine learning and artificial intelligence techniques

Yin, Tim [Pacific Northwest National Laboratory (P↗
DOE OSTI2019

All Hazards Analysis Core

The All Hazards Knowledge Framework (AHA) is a dynamic analytical framework that enables critical infrastructure knowledge discovery and decision support across the five mission areas – prevention, protection, mitigation, response and recovery. AHA provides the ability to store and model infrastructure systems as a linked multigraphs providing an intuitive and natural representation. This capability provides the foundation to rapidly evaluate and understand the potential consequences of manmade and natural disaster on these systems.

McGillivary, KentE.↗
DOE OSTI2011

Augmented cognition tool for rapid military decision making.

This report describes the laboratory directed research and development work to model relevant areas of the brain that associate multi-modal information for long-term storage for the purpose of creating a more effective, and more automated, association mechanism to support rapid decision making. Using the biology and functionality of the hippocampus as an analogy or inspiration, we have developed an artificial neural network architecture to associate k-tuples (paired associates) of multimodal input records. The architecture is composed of coupled unimodal self-organizing neural modules that learn generalizations of unimodal components of the input record. Cross modal associations, stored as a higher-order tensor, are learned incrementally as these generalizations form. Graph algorithms are then applied to the tensor to extract multi-modal association networks formed during learning. Doing so yields a novel approach to data mining for knowledge discovery. This report describes the neurobiological inspiration, architecture, and operational characteristics of our model, and also provides a real world terrorist network example to illustrate the model's functionality.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2014

US Woody Crop Yield Potential Database Documentation with Referenced Yield Summary Tables

A woody crop yield potential (WCYP) database was created containing yield results with as much associated information as was available concerning the sites, soils, and experimental treatments. The database summarizes a very broad set of old and new standing biomass data from plantation-grown hardwoods and softwoods established under a wide range of conditions across the United States and Canada. The WCYP database, together with this document, is being published to disseminate information on what is available in the literature with respect to yield evaluations and to inform people that not all yield data in the open literature are suitable for evaluation of “potential” regional yields. It has also been presented with the hope that much more information will be collected and added to the WCYP database. The current set of data in the WCYP database is derived from 41 unique reference sources describing 79 experiments in the United States or southern Canada with a total of 473 different treatments. The database contains several worksheets. The worksheet that reports the complete yield curves contains roughly 1,660 rows of data and 81 columns of parameters reporting yields and associated treatment factors. Separate worksheets, linked by the source publication author and year, describe the sites and soils associated with each experiment; another separate worksheet assembles only the final yields deemed appropriate for yield potential analysis. There are additional worksheets with conversion factors relevant to the database and notes pertaining to decisions made regarding organization of the data. This technical manuscript describes how the information was assembled to create the WCYP database that is simultaneously being published on the US Department of Energy (DOE) Bioenergy Knowledge Discovery Framework website maintained by Oak Ridge National Laboratory (ORNL). A summary table with links to all worksheets in the database can be found at the web address https://www.bioenergykdf.net/WCYP .

09 BIOMASS FUELS↗
DOE OSTI2014

SciDAC Institute for Ultra-Scale Visualization: Activity Recognition for Ultra-Scale Visualization

Understanding the science behind ultra-scale simulations requires extracting meaning from data sets of hundreds of terabytes or more. Developing scalable parallel visualization algorithms is a key step enabling scientists to interact and visualize their data at this scale. However, at extreme scales, the datasets are so huge, there is not even enough time to view the data, let alone explore it with basic visualization methods. Automated tools are necessary for knowledge discovery -- to help sift through the information and isolate characteristic patterns, thereby enabling the scientist to study local interactions, the origin of features and their evolution in large volumes of data. These tools must be able to operate on data of this scale and work with the visualization process. In this project, we developed a framework for activity detection to allow scientists to model and extract spatio-temporal patterns from time-varying data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Towards Exascale: High Performance Visualization and Analytics – Project Status Report

The LBNL Visualization research program focuses on a key set of problems facing our community as we move towards the exascale regime of computing. First, we aim to better understand how to effectively take advantage of an evolving architectural landscape where there are an increasing number of cores per processor by examining algorithmic reformulation and alternative methods of programming these increasingly complex platforms. Second, as the gap widens between computational and I/O capacity, it is increasingly imperative to perform more visual data exploration and analysis operations concurrent with the simulation to avoid increasingly expensive I/O. Our approach is to examine several interrelated issues aimed at minimizing the memory footprint, data movement, and design/implementation issues related to deploying tools that can have broad applicability across many different simulation codes and science domains. Third, increasing computational power produces larger and more complex scientific data, and our research program focuses on new technologies aimed at enabling scientific data understanding with ever-larger data. Here, we explore how multi-resolution data representation techniques can reduce data movement, and study novel approaches that focus visualization and analysis processing on subsets of data that are “scientifically interesting,” thereby also minimizing data movement while accelerating scientific knowledge discovery.

97 MATHEMATICS AND COMPUTING↗