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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Sparse Tensor Benchmark Suite for CPUs and GPUs

Tensor computations present significant performance chal- lenges that impact a wide spectrum of applications ranging from machine learning, healthcare analytics, social network analysis, data mining to quantum chemistry and signal processing. Efforts to improve the perfor- mance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. This work presents a benchmark suite for arbitrary-order sparse tensor kernels using state- of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO) on CPUs and GPUs. It presents a set of reference tensor kernel implementations that are compatible with real-world tensors and power law tensors extended from synthetic graph generation techniques. We also propose Roofline performance models for these kernels to provide insights of computer platforms from sparse tensor view. This benchmark suite along with the synthetic tensor generator is publicly available.

Li, Jiajia↗
DOE OSTI2019

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed. Finally, the proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state-of-the-art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes.

42 ENGINEERING↗
DOE OSTI2018

Medical diagnoses using three-way fuzzy concept lattice and their Euclidean distance

Searching the closest symptoms of a particular disease in the given patient is a major concern for medical diagnoses expert. In most cases, this problem arises due to large number of incomplete, uncertain, or inconsistent information generated in medical diagnoses data set. Recently, some of the researchers tried to pay attention towards precise measurement of uncertainty and vagueness in medical data set using the properties of applied abstract algebra. In this process, researchers noticed that calculus of three-way decision space is more useful to characterize the uncertainty and vagueness in medical data set based on truth, indeterminacy, and falsity membership-values. However, searching some relevant query based on user requirement to diagnoses the disease is a major issue in three-way decision space. To encounter this problem, current paper tried to analyze the medical data set using the properties of single-valued neutrosophic graph-based concept lattice. In addition, another method is proposed to select some of the interesting three-way fuzzy concepts at user-defined granulation for their computed Euclidean distance with an illustrative example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

SPARTAN (Scalable Probabilistic Application Reconfigurable Tensor Autonomous Network)

The technical founder of Ludwig Computing Inc has been competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Ludwig Computing Inc, supported by the U.S. Department of Energy's Advanced Manufacturing Office through the Cyclotron Road program, has investigated the advantages of probabilistic computing for real-world compute-intensive applications. This research adds to the understanding of alternative computing paradigms by exploring a unique hardware-software co-design that integrates quantum computing methods with nature-inspired problem-solving techniques. The project's focus on areas such as combinatorial optimization, graph analytics, and machine learning demonstrates the potential for significant advancements in computational efficiency and performance. By harnessing natural randomness to streamline large circuits into fewer devices, Ludwig's approach enables massive parallelism, potentially offering higher throughput, speed, and energy efficiency compared to conventional hardware solutions. This work benefits the public by paving the way for more efficient computing solutions that could address complex real-world problems while potentially reducing energy consumption in data-intensive industries.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine Learning Modeling Pipeline for Extracting Nuclear Proliferation Events of Interest from Open Data Sources (U)

In FY2020, the Savannah River National Laboratory (SRNL) and the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Polytechnic Institute and State University entered a collaboration funded by Department of Energy’s (DOE) Office of Defense Nuclear Nonproliferation Research and Development. The project’s mission was to take the first steps toward developing a demonstration prototype system that uses multiple machine learning and data analytics methods on largescale open data sources to identify new, developing, and/or undeclared nuclear programs. Given the SRNL team’s on-site perspective of events culminating in the DOE’s decision to pursue the Savannah River Plutonium Processing Facility (SRPPF), the team targeted the identification of events and indicators in retrospective datasets that pointed to the activity of “fissile core fabrication at the Savannah River Site” prior to the official announcement in May of 2018. A preliminary modeling pipeline was developed in FY20 that showed the datasets contained adequate signal for continuation of efforts. In FY21, a modular demonstration prototype modeling pipeline has continued in development for two text-based data sources: a broad internet archive (Webhose Ltd.) and a decahose Twitter database (i.e., a global sampling of one in every ten Tweets). The techniques that have been developed rely on graph theory and anomaly detection to identify contextual shifts in key words and phrases at various points in time such that indicators of events of interest could be identified and subsequently, events could be extracted from the corpuses. The foundational concept behind the approaches is that contextual shifts in key words and phrases can act as indicators of events of interest. Both datasets have proven successful in extracting events of interest related to pit production at the Savannah River Site prior to the official announcement. In addition, the pipelines have generated a wide range of events broadly summarized as: the awarding of DOE contracts at major sites, DOE investments in various programs, accidents at DOE national laboratories, speculations about the fate of pit production in the DOE complex, domestic and international shipments and receipts of nuclear materials at DOE sites, termination of non-proliferation agreements with Russia, termination of MOX, new weapons development approvals/testing, nuclear posture reviews, major DOE cleanup/production milestones, political opinions, and nuclear watch groups’ opinions, among many others.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A sorghum practical haplotype graph facilitates genome‐wide imputation and cost‐effective genomic prediction

Abstract Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to accelerate cultivar development. To help with this, we developed a Sorghum bicolor Practical Haplotype Graph (PHG) pangenome database that stores haplotypes and variant information. We developed two PHGs in sorghum that were used to identify genome‐wide variants for 24 founders of the Chibas sorghum breeding program from 0.01x sequence coverage. The PHG called single nucleotide polymorphisms (SNPs) with 5.9% error at 0.01x coverage—only 3% higher than PHG error when calling SNPs from 8x coverage sequence. Additionally, 207 progenies from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes were imputed from PHG parental haplotypes and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from .57–.73 and are similar to prediction accuracies obtained with genotyping‐by‐sequencing or targeted amplicon sequencing (rhAmpSeq) markers. This study demonstrates the use of a sorghum PHG to impute SNPs from low‐coverage sequence data and shows that the PHG can unify genotype calls across multiple sequencing platforms. By reducing input sequence requirements, the PHG can decrease the cost of genotyping, make GS more feasible, and facilitate larger breeding populations. Our results demonstrate that the PHG is a useful research and breeding tool that maintains variant information from a diverse group of taxa, stores sequence data in a condensed but readily accessible format, unifies genotypes across genotyping platforms, and provides a cost‐effective option for genomic selection.

Jensen, Sarah E.↗
DOE OSTI2023

Global horizontal spectral irradiance and module spectral response measurements: an open dataset for PV research

This report describes the creation process and final content of a spectral irradiance dataset for Albuquerque, New Mexico accompanied by a set of spectral response measurements for modules deployed at the same location. The spectral irradiance measurements were made using horizontally mounted spectroradiometers; therefore, they represent global horizontal irradiance. The dataset combines non-continuous spectroradiometer and weather measurements from a two-year period into a single calendar year. The data files are accompanied by extensive metadata as well as example calculations and graphs to demonstrate the potential uses of this database. The spectral response measurements were carried out by the National Renewable Energy Laboratory using 12 commercial silicon modules types that are undergoing long-term evaluation at Sandia National Laboratories in Albuquerque.

14 SOLAR ENERGY↗
DOE OSTI2021

A Survey: Handling Irregularities in Neural Network Acceleration with FPGAs

In the last decade, Artificial Intelligence (AI) through Deep Neural Networks (DNNs) has penetrated virtually every aspect of science, technology, and business. Many types of DNNs have been and continue to be developed, including Convolutional Neural Networks (CNNs), Recurrent Neural Net- works (RNNs), and Graph Neural Networks (GNNs). The overall problem for all of these Neural Networks (NNs) is that their target applications generally pose stringent constraints on latency and throughput, while also having strict accuracy requirements. There have been many previous efforts in creating hardware to accelerate NNs. The problem designers face is that optimal NN models typically have significant irregularities, making them hardware-unfriendly. In this paper, we first define the problems in NN acceleration by characterizing common irregularities in NN processing into 4 types; then we summarize the existing works that handle the four types of irregularities efficiently using hardware, especially FPGAs; finally, we provide a new vision of next-generation FPGA-based NN acceleration: that the emerging heterogeneity in the next-generation FPGAs is the key to achieving higher performance.

Geng, Tong↗
DOE OSTI2018

Random Walk on a Perturbation of the Infinitely-Fast Mixing Interchange Process

We consider a random walk in dimension d≥1 in a dynamic random environment evolving as an interchange process with rate γ>0. We prove that, if we choose γ large enough, almost surely the empirical velocity of the walker X{sub t}/t eventually lies in an arbitrary small ball around the annealed drift. This statement is thus a perturbation of the case γ=+∞ where the environment is refreshed between each step of the walker. We extend three-way part of the results of Huveneers and Simenhaus (Electron J Probab 20(105):42, 2015), where the environment was given by the 1-dimensional exclusion process: (i) We deal with any dimension d≥1; (ii) We treat the much more general interchange process, where each particle carries a transition vector chosen according to an arbitrary law μ; (iii) We show that X{sub t}/t is not only in the same direction of the annealed drift, but that it is also close to it.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Effects of Dead‐End Fractures on Non‐Fickian Transport in Three‐Dimensional Discrete Fracture Networks

Abstract Understanding mechanistic causes of non‐Fickian transport in fractured media is important for many hydrogeologic processes and subsurface applications. This study elucidates the effects of dead‐end fractures on non‐Fickian transport in three‐dimensional (3D) fracture networks. Although dead‐end fractures have been identified as low‐velocity regions that could delay solute transport, the direct relation between dead‐end fractures and non‐Fickian transport has been elusive. We systematically generate a large number of 3D discrete fracture networks with different fracture length distributions and fracture densities. We then identify dead‐end fractures using a novel graph‐based method. The effect of dead‐end fractures on solute residence time maximizes at the critical fracture density of the percolation threshold, leading to strong late‐time tailing. As fracture density increases beyond the percolation threshold, the network connectivity increases, and dead‐end fractures diminish. Consequently, the increase in network connectivity leads to a reduction in the degree of late‐time tailing. We also show that dead‐end fractures can inform about main transport paths, such as the mean tortuosity of particle trajectories. This study advances our mechanistic understanding of solute transport in 3D fracture networks.

58 GEOSCIENCES↗
DOE OSTI2019

STEMDL

The code "STEMDL" provides highly-optimized multi-gpu implementations of DL models of applicability in electron microscopy via the use of mixed-precision arithmetic, native graph construction and evaluation in Tensorflow, and adaptive scaling of learning parameters to avoid numerical instabilities due to reduced precision. STEMDL also uses low-level methods from the Horovod library for gradient communication and reduction and adds an additional optimization by allowing the user to scheduling the gradient reduction operations based on their position within the layers of a neural net. STEMDL provides optimized I/O routines for data staging and data reading/writing in both Tensorflow and Pytorch using multi-processing and asynchronous transfer between host and device. .

Laanait, Nouamane↗
DOE OSTI2019

An Agile Design-to-Simulation Workflow Using a New Conforming Moving Least Squares Method

This report summarizes the accomplishments and challenges of a two year LDRD effort focused on improving design-to-simulation agility. The central bottleneck in most solid mechanics simulations is the process of taking CAD geometry and creating a discretization of suitable quality, i.e., the "meshing" effort. This report revisits meshfree methods and documents some key advancements that allow their use on problems with complex geometries, low quality meshes, nearly incompressible materials or that involve fracture. The resulting capability was demonstrated to be an effective part of an agile simulation process by enabling rapid discretization techniques without increasing the time to obtain a solution of a given accuracy. The first enhancement addressed boundary-related challenges associated with meshfree methods. When using point clouds and Euclidean metrics to construct approximation spaces, the boundary information is lost, which results in low accuracy solutions for non-convex geometries and mate rial interfaces. This also complicates the application of essential boundary conditions. The solution involved the development of conforming window functions which use graph and boundary information to directly incorporate boundaries into the approximation space.

42 ENGINEERING↗
DOE OSTI2020

Evaluation of Methods for Causal Discovery in Hydrometeorological Systems

Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph-based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Abstract We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

High performance Data Driven Agent-based Modeling Framework for Simulation of Commute Mode Choices in Metropolitan Area

Active transportation, human-powered transportation modes such as walking and bicycling, not only reduces the carbon footprint from the transportation sector but also promotes healthy living by offering opportunities for people to build physical activity into their daily routine. To encourage active transportation through urban planning and public campaigns, it is of significant importance to infer factors that substantially influence commuters in their transportation mode choice process. This necessitates a flexible and repeatable tool that can evaluate how a policy is perceived by individual commuters and convert their decisions into macro level understanding. This paper introduces one such effort that is specifically designed for studies of transportation mode choices in metropolitan areas. It provides results from a high-resolution data driven simulation based on high performance computing implementation of the agent-based model framework for home-to-work commute trips. The framework uses a graph-partition based technique that can leverage the interaction structure of agents within a geographic proximity and can boost the simulation execution time. Further, based on a flexible design, it can run ABM with different levels of computing resources-from multi core workstations to an HPC grid. The framework has been tested on the Titan Cray XK7 supercomputer of the Oak Ridge Leadership Computing Facility.

Park, Byung↗
DOE OSTI2019

Parsl: Pervasive Parallel Programming in Python

High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. This migration towards orchestration rather than implementation, coupled with the growing need for parallel computing (e.g., due to big data and the end of Moore's law), necessitates rethinking how parallelism is expressed in programs. Here, we present Parsl, a parallel scripting library that augments Python with simple, scalable, and flexible constructs for encoding parallelism. These constructs allow Parsl to construct a dynamic dependency graph of components that it can then execute efficiently on one or many processors. Parsl is designed for scalability, with an extensible set of executors tailored to different use cases, such as low-latency, high-throughput, or extreme-scale execution. We show, via experiments on the Blue Waters supercomputer, that Parsl executors can allow Python scripts to execute components with as little as 5 ms of overhead, scale to more than 250000 workers across more than 8000 nodes, and process upward of 1200 tasks per second. Other Parsl features simplify the construction and execution of composite programs by supporting elastic provisioning and scaling of infrastructure, fault-tolerant execution, and integrated wide-area data management. We show that these capabilities satisfy the needs of many-task, interactive, online, and machine learning applications in fields such as biology, cosmology, and materials science.

parallel programming↗
DOE OSTI2021

A Systems Approach to Estimating the Uncertainty Limits of X-Ray Radiographic Metrology

Micro- and nanomanufacturing capabilities have rapidly expanded over the past decade to include complex three-dimensional (3D) structure fabrication; however, the metrology required to accurately assess these processes via part inspection and characterization has struggled to keep pace. X-ray computed tomography (CT) is considered an ideal candidate for providing the critically needed metrology on the smallest scales, especially internal features, or inaccessible regions. X-ray CT supporting micro- and nanomanufacturing often push against the poorly understood resolution and variation limits inherent to the machines, which can distort or hide fine structures. In this study, we have developed and experimentally verify a comprehensive analytical uncertainty propagation signal variation flow graph (SVFG) model for X-ray radiography in this work to better understand resolution and image variability limits on the small scale. The SVFG approach captures, quantifies, and predicts variations occurring in the system that limit metrology capabilities, particularly in the micro/nanodomain. This work is the first step to achieving full uncertainty modeling of CT reconstructions and provides insight into improving X-ray attenuation imaging systems. The SVFG methodology framework is applied to generate a complete basis set of functions describing the major sources of variation in radiographs. Five models are identified, covering variation in energy, intensity, length, blur, and position. Radiographic system experiments are defined to measure the parameters required by the SVFGs. Best practices are identified for these measurements. The SVFG models are confirmed via direct measurement of variation to predict variation within 30% on average.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

Many geological materials, such as shale, mudstone, carbonate rock, limestone and rock salt are multi-porosity porous media in which pores of different scales may co-exist in the host matrix. When fractures propagate in these multi-porosity materials, these pores may enlarge and coalesce and therefore change the magnitude and the principal directions of the effective permeability tensors. The pore-fluid inside the cracks and the pores of host matrix may interact and exchange fluid mass, but the difference in hydraulic properties of these pores often means that a single homogenized effective permeability tensor field is insufficient to characterize the evolving hydraulic properties of these materials at smaller time scale. Furthermore, the complexity of the hydro-mechanical coupling process and the induced mechanical and hydraulic anisotropy originated from the micro-fracture and plasticity at grain scale also makes it difficult to propose, implement and validate separated macroscopic constitutive laws for numerical simulations. Here this article presents a hybrid data-driven method designed to capture the multiscale hydro-mechanical coupling effect of porous media with pores of various different sizes. At each scale, data-driven models generated from supervised machine learning are hybridized with classical constitutive laws in a directed graph that represents the numerical models. By using sub-scale simulations to generate database to train material models, an offline homogenization procedure is used to replace the up-scaling procedure to generate cohesive laws for localized physical discontinuities at both grain and specimen scales. Through a proper homogenization procedure that preserves spatial length scales, the proposed method enables field-scale simulations to gather insights from meso-scale and grain-scale micro-structural attributes. This method is proven to be much more computationally efficient than the classical DEM–FEM or FEM2 approach while at the same time more robust and flexible than the classical surrogate modeling approach. Due to the usage of bridging-scale technique, the proposed model may provide multiple opportunities to incorporate different types of simulations and experimental data across different length scales for machine learning. Numerical issues will also be discussed.

42 ENGINEERING↗