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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Learning dynamical systems from data: An introduction to physics-guided deep learning

Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, and sample-efficient. However, they often rely on strong modeling assumptions and expensive numerical integration, requiring significant computational resources and domain expertise. While deep learning (DL) provides efficient alternatives for modeling complex dynamics, they require a large amount of labeled training data. Furthermore, its predictions may disobey the governing physical laws and are difficult to interpret. Physics-guided DL aims to integrate first-principled physical knowledge into data-driven methods. It has the best of both worlds and is well equipped to better solve scientific problems. Recently, this field has gained great progress and has drawn considerable interest across discipline Here, we introduce the framework of physics-guided DL with a special emphasis on learning dynamical systems. We describe the learning pipeline and categorize state-of-the-art methods under this framework. We also offer our perspectives on the open challenges and emerging opportunities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

CARDS: A blueprint and environment for domain-specific software reuse

CARDS (Central Archive for Reusable Defense Software) exploits advances in domain analysis and domain modeling to identify, specify, develop, archive, retrieve, understand, and reuse domain-specific software components. An important element of CARDS is to provide visibility into the domain model artifacts produced by, and services provided by, commercial computer-aided software engineering (CASE) technology. The use of commercial CASE technology is important to provide rich, robust support for the varied roles involved in a reuse process. We refer to this kind of use of knowledge representation systems as supporting 'knowledge-based integration.'

Wallnau, Kurt C.↗
NASA NTRSDate not supplied

A Hybrid Approach to Labeling Datasets in Earth Science Publications

NASA Data Centers provide the public with thousands of datasets that result in published papers, reports, and conference proceedings. Collecting accurate metrics on usage of these datasets is key to connecting different areas of knowledge and evaluating the datasets’ impact. While most of the datasets have Digital Object Identifiers (DOIs) assigned, most publications do not cite them hampering the automated search of these publications. Instead, articles mention attributes like organization, instrument, mission, variable, or a publication describing the dataset. Often only domain experts can deduce the dataset that was used in the publication text. The lack of a citation slows the spread of information and reduces the research’s impact. With thousands of papers produced each year, an automated means of labeling datasets is critical. This paper explores a hybrid approach of heuristics and a Natural Language Processing (NLP) Named Entity Recognition (NER) model to find and label the datasets used within Earth Science papers. Heuristics are used to produce the labelled sentences and any potential dataset candidates that can be derived from a sentence. The heuristic labels the sentences with the names of mission, instrument, re-analysis models, and science keywords taken from the Global Change Master Directory (GCMD) ontology. Additionally, it uses those labels to generate the dataset citation candidates. If the mission, instrument, and variable are sufficient to create the citation for the dataset the citation and the label the domain expert reviews the output without going through the NLP model. If the extracted label is not sufficient to label the dataset on its own, the sentence and its associated dataset labels will be inputted into the NER model. The model outputs the labeled sentence and the potential dataset candidates with their associated probabilities. The domain expert then reviews the NER model’s output and the correct labels are determined. The newly labelled papers can then be used as additional training data. This creates an iterative process for the approach to continuously improve. Because all the possible mentions are gathered by the model, the domain expert can quickly and easily label the papers resulting in large time savings.

Jacob Atkins↗
DOE OSTI2022

Towards automating structural discovery in scanning transmission electron microscopy *

Abstract Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO 3 matrix, ferroelectric domains in BiFeO 3 , and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows .

47 OTHER INSTRUMENTATION↗
NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗
DOE OSTI2020

Compiler and Runtime Approaches to Enable Large-Scale Irregular Programs. Final report, July 2013 - July 2019

While regular algorithms, characterized by operations on dense matrices and arrays, have long been the mainstay of scientific, high-performance computing, irregular algorithms, which feature unpredictable accesses to pointer-based data structures, are becoming increasingly common in high performance computing, arising in graph analysis, data mining and visualization, among other domains. Unfortunately, the defining characteristics of irregular applications, their dynamic, unpredictable, data-dependent access patterns and data layouts, make achieving high performance on large scale systems difficult. Scaling applications to peta- and exa-scale requires carefully controlling communication and data movement and placement, an inherently difficult task when access patterns and data layouts are unpredictable! Most irregular applications that attain high performance must be painstakingly hand-written and hand-tuned, with few common principles or paradigms uniting various implementations and easing future development. Despite the increasing importance of irregular applications, there is little programmer knowledge, and even less compiler ability, devoted to optimizing them. This project aims to solve these problems. By allowing programmers to write irregular applications in high level forms, with at most a few annotations highlighting key structural properties, programmers can focus on developing their algorithms and methods. The compiler and run-time system can take on the tedious task of optimizing the application for execution at large scales, and can automatically provide efficient implementations. This will provide portability and ease maintenance for existing irregular applications, but, more importantly, open up whole new domains of computational science to large-scale, high-performance simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A database of battery materials auto-generated using ChemDataExtractor

A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.

25 ENERGY STORAGE↗
NASA NTRS1987

The generic task toolset: High level languages for the construction of planning and problem solving systems

The current generation of languages for the construction of knowledge-based systems as being at too low a level of abstraction is criticized, and the need for higher level languages for building problem solving systems is advanced. A notion of generic information processing tasks in knowledge-based problem solving is introduced. A toolset which can be used to build expert systems in a way that enhances intelligibility and productivity in knowledge acquistion and system construction is described. The power of these ideas is illustrated by paying special attention to a high level language called DSPL. A description is given of how it was used in the construction of a system called MPA, which assists with planning in the domain of offensive counter air missions.

Chandrasekaran, B.↗
NASA NTRS1990

Constructing an advanced software tool for planetary atmospheric modeling

A prototype is described that can serve as a scientific-modeling software tool to facilitate the development of useful scientific models. The prototype is developed for applications to planetary modeling, and specific examples are given that relate to the atmosphere of Titan. The scientific modeling tool employs a high-level domain-specific modeling language, several data-display facilities, and a library of experimental datasets and scientific equations. The planetary modeling prototype links uncomputed physical variables to computed variables with computational transformations based on a backchaining procedure. The system - implemented in LISP with an object-oriented knowledge-representation tool - is run on a workstation that provides interface with several models. The prototype is expected to form the basis for a sophisticated modeling tool that can permit active experimentation.

Keller, Richard M.↗
NASA NTRS2019

Improving Earth Science Data Literacy

The improvement of scientific literacy across the public can take on a myriad of forms and activities. The vast archives of NASA’s Earth science data, as one example, reflect the agency’s ongoing commitment to producing the highest quality data, services and tools intended for enabling the public to understand the complexity of Earth systems. The focus of this presentation is to explore and reveal the specific strategies the NASA Earth Science Data and Information System (ESDIS) Project uses to support our very large and diverse user communities access to and understanding of these science data and services. The diversity of our subject matter, spanning the whole Earth science domain, is mirrored by our user communities which are equally as broad in their needs and abilities. ESDIS and the supporting Communication Team leverages an array of internet-based tools and communication strategies to both understand actual user needs and develop optimum pathways for improving our users’ knowledge and understanding of the data. We will focus our limited time on a few of the high-impact activities including Webinars, or video-based, on-line, interactive discussions and presentations that since their introduction in 2013 have become a staple for user-instructor interaction via the internet. Here we discuss the types of content we produce and many lessons learned on how best to use this medium for improving user literacy on the subject at hand. In addition, we include discussion of our approach to using various social media platforms and tools to bolster the awareness of our users for data and services that are likely of interest. The ESDIS project remains active in various social media campaigns where our particular blend of science, data and tools engages new users curious of how to get their science or application started using NASA-available data. We anticipate that these examples may prove to be novel in their application to improving science literacy and skill development among the public.

Lindsay, Francis↗
NASA NTRSDate not supplied

Standards Roadmapping for Mission Assurance in Commercial Spaceflight

This paper explores the application of established roadmapping approaches in standards planning and roadmap development to support mission assurance objectives. Mission assurance requires proactive planning across multiple domains including technical, schedule, cost, organizational, and policy. The intersection of these domains happens at the enterprise level, and enterprise systems engineering processes can systematically guide mission assurance activities, including development of standards roadmaps. Standards planning and development is a critical contributor to an effective mission assurance strategy, although standards are often developed and adopted reactively. Technology and standards planning is a key Enterprise Process Management activity, as identified by the Systems Engineering Body of Knowledge, and technology and standards roadmaps are strategic tools used to enable that process. Technology roadmapping has proven effective in guiding enterprise architecture and concept design, and standards planning supports enterprise requirements definition and management. However, there are limited methodologies to guide such standards planning activities. The application of technology roadmapping principles to standards planning formalizes the process and establishes a repeatable framework for standards development and integration. The framework development described in this paper fills a methodological gap by adapting proven roadmapping techniques to standards planning. Through a comparison of roadmapping methodologies and a case study analysis, this approach outlines a systematic process for anticipating and planning standards needs. In the case of developing a standards integration roadmap for NASA’s Office of Safety and Mission Assurance (OSMA), technology roadmapping principles were adapted to develop a roadmap framework to allow NASA and OSMA to define an agency-wide standards integration plan for commercial and industry safety and mission assurance standards. The outcome highlights key adaptations required for roadmapping in a standards context and describes the process steps to formalize a standards planning activity. The roadmapping framework adapted for standards development and integration enables more strategic, forward-looking standards planning and allows for better alignment between evolving capabilities and mission assurance requirements. Beyond its application to mission assurance, this methodology can be transferred to other domains requiring standards development, providing a systematic approach that integrates systems engineering principles into policy strategy and decision-making processes.

Enterprise Systems Engineering↗
DOE OSTI2020

Deffe: A Data-efficient Framework for Performance Characterization in Domain-Specific Computing

Predicting workload performance is a crucial task for many architecture and system research. In this paper we present De￿e, a framework to estimate the workload performances under varying architectural configurations. The infrastructure component of Deffe is based on scalable and easy-to-use open-source software components. By casting the performance modeling as transfer learning tasks, the modeling component of Deffe can leverage the learned knowledge on one workload, and “transfer” it to a new workload. Extensive experimental results show that the method can achieve superior testing accuracy with an effective reduction of 32-80x in terms of the amount of required training data.

Liu, Frank↗
NASA NTRS1991

Distributed, cooperating knowledge-based systems

Some current research in the development and application of distributed, cooperating knowledge-based systems technology is addressed. The focus of the current research is the spacecraft ground operations environment. The underlying hypothesis is that, because of the increasing size, complexity, and cost of planned systems, conventional procedural approaches to the architecture of automated systems will give way to a more comprehensive knowledge-based approach. A hallmark of these future systems will be the integration of multiple knowledge-based agents which understand the operational goals of the system and cooperate with each other and the humans in the loop to attain the goals. The current work includes the development of a reference model for knowledge-base management, the development of a formal model of cooperating knowledge-based agents, the use of testbed for prototyping and evaluating various knowledge-based concepts, and beginning work on the establishment of an object-oriented model of an intelligent end-to-end (spacecraft to user) system. An introductory discussion of these activities is presented, the major concepts and principles being investigated are highlighted, and their potential use in other application domains is indicated.

Truszkowski, Walt↗
NASA NTRS2003

Team Formation and Communication Restrictions in Collectives

A collective of agents often needs to maximize a "world utility" function which rates the performance of an entire system, while subject to communication restrictions among the agents. Such communication restrictions make it difficult for agents which try to pursue their own "private" utilities to take actions that also help optimize the world utility. Team formation presents a solution to this problem, where by joining other agents, an agent can significantly increase its knowledge about the environment and improve its chances of both optimizing its own utility and that its doing so will contribute to the world utility. In this article we show how utilities that have been previously shown to be effective in collectives can be modified to be more effective in domains with moderate communication restrictions resulting in performance improvements of up to 75%. Additionally we show that even severe communication constraints can be overcome by forming teams where each agent of a team shares the same utility, increasing performance an additional 25%. We show that utilities and team sizes can be manipulated to form the best compromise between how "aligned" an agent s utility is with the world utility and how easily an agent can learn that utility.

Agogino, Adrian K.↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-plane thermal conductivity and the applicability of the Wiedemann–Franz law in dilute AlCu thin films

The Wiedemann–Franz (WF) law correlates heat and charge transport in metals. However, the validity of this correlation remains an open-ended question, especially in the context of inelastic scattering at room temperature. To address this gap in knowledge, we perform independent measurements of the in-plane thermal and electrical conductivities across four AlCu (0.5% Cu) films [thickness (⁠h⁠) ≈ 174, 98, 53, and 24 nm] using optical pump–probe metrologies and four-point probe techniques, respectively. For in-plane thermal conductivity measurements, we utilize time-domain thermoreflectance, in both concentric and beam-offset configurations, and the time-resolved magneto-optic Kerr effect. Our results show that the WF law overpredicts the thermal conductivity by at least ∼10% in all films, thus demonstrating modest deviations in predicted thermal conductivity when applying the WF law to dilute AlCu films. Using infrared variable angle spectroscopic ellipsometry, we demonstrate increased electron scattering rates in the thinnest film (⁠⁠h⁠ ≈ 24 nm), indicating electron-boundary scattering drives the reduction in in-plane thermal conductivity. Furthermore, this is generally an elastic scattering process, which is supported by our thermal conductivity measurements and analysis.

Electrical conductivity↗
DOE OSTI2023

A Neural Differential Equation Formulation for Modeling Atmospheric Effects in Hyperspectral Images

Atmospheric correction is the process for removing atmospheric effects from spectral data; a necessary step for recovering salient spectral properties. The complex interactions between the atmosphere and light are dominated by absorbance and scattering physics. Existing methods for modeling atmospheric interactions typically rely on deep knowledge of relevant environmental conditions and high-fidelity numerical simulations of the governing physics in order to obtain accurate estimates of these effects. Additionally, existing approaches often require a subject matter expert for pre/post-processing of the data. Model-based approaches for removing atmospheric effects struggle in situations where such domain expertise is not available, and require significant human effort and computational power even when that expertise is available. In contrast, we propose a data-driven approach the uses Neural Differential Equations (NDEs) to accurately learn the interactions between electromagnetic radiation and the atmospheric without access to location specific environmental information. Once trained, the NDE can be applied bi-directionally; to apply or remove atmospheric effects. We demonstrate the effectiveness and utility of these techniques on an example multi-spectral scene.

Koch, James V.↗