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NASA NTRS1989

The inverse gravimetric problem in gravity modelling

One of the main purposes of geodesy is to determine the gravity field of the Earth in the space outside its physical surface. This purpose can be pursued without any particular knowledge of the internal density even if the exact shape of the physical surface of the Earth is not known, though this seems to entangle the two domains, as it was in the old Stoke's theory before the appearance of Molodensky's approach. Nevertheless, even when large, dense and homogeneous data sets are available, it was always recognized that subtracting from the gravity field the effect of the outer layer of the masses (topographic effect) yields a much smoother field. This is obviously more important when a sparse data set is bad so that any smoothing of the gravity field helps in interpolating between the data without raising the modeling error, this approach is generally followed because it has become very cheap in terms of computing time since the appearance of spectral techniques. The mathematical description of the Inverse Gravimetric Problem (IGP) is dominated mainly by two principles, which in loose terms can be formulated as follows: the knowledge of the external gravity field determines mainly the lateral variations of the density; and the deeper the density anomaly giving rise to a gravity anomaly, the more improperly posed is the problem of recovering the former from the latter. The statistical relation between rho and n (and its inverse) is also investigated in its general form, proving that degree cross-covariances have to be introduced to describe the behavior of rho. The problem of the simultaneous estimate of a spherical anomalous potential and of the external, topographic masses is addressed criticizing the choice of the mixed collection approach.

Sanso, F.↗

DOE OSTI2021

Titan on the Red (advanced AI / ML system) [Slides]

Born out of a need to make LANL Weapons Program archival material available to scientists and engineers. Material is the end result of decades of consolidation of mini-libraries and mini archives at LANL. Latest consolidation brought together LANL’s digital archives and physical archives. This houses over 75 years of nuclear weapons research, designs, procedures, videos, photos, and other reports. For the Titan on the Red machine learning project, the system must be able to automatically extract metadata from digitized documents, perform natural-language search, enforce security classification and NTK protocols, be expandable to ingest various data stores (Online Vault, PDMLink, shared drives, SharePoint, etc.), and utilize commercial, public domain, and LANL ontologies.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

NASA NTRS2014

U.S. Spacesuit Knowledge Capture Accomplishments in Fiscal Years 2012 and 2013

The NASA U.S. Spacesuit Knowledge Capture (KC) program has existed since the beginning of 2008. The program was designed to augment engineers and other technical team members with historical spacesuit information to add to their understanding of the spacesuit, its evolution, its limitations, and its capabilities. Over 40 seminars have captured spacesuit history and knowledge over the last six years of the program's existence. Subject matter experts have provided lectures and some were interviewed to help bring the spacesuit to life so that lessons learned will never be lost. As well, the program concentrated in reaching out to the public and industry by making the recorded events part of the public domain through the NASA technical library through YouTube media. The U.S. Spacesuit KC topics have included lessons learned from some of the most prominent spacesuit experts and spacesuit users including current and former astronauts. The events have enriched the spacesuit legacy knowledge from Gemini, Apollo, Skylab, Space Shuttle and International Space Station Programs. As well, expert engineers and scientists have shared their challenges and successes to be remembered. Based on evidence by the thousands of people who have viewed the recordings online, the last few years have been some of the most successful years of the KC program's life with numerous digital recordings and public releases. This paper reviews the events accomplished and archived over Fiscal Years 2012 and 2013 and highlights a few of the most memorable ones. This paper also communicates ways to access the events that are available internally on the NASA domain as well as those released on the public domain.

Chullen, Cinda↗

NASA NTRS2014

U.S. Spacesuit Knowledge Capture Accomplishments in Fiscal Years 2012 and 2013

The NASA U.S. spacesuit knowledge capture (KC) program has been in operations since the beginning 2008. The program was designed to augment engineers and others with information about spacesuits in a historical way. A multitude of seminars have captured spacesuit history and knowledge over the last six years of the programs existence. Subject matter experts have provided lectures and were interviewed to help bring the spacesuit to life so that lessons learned will never be lost. As well, the program concentrated in reaching out to the public and industry by making the recorded events part of the public domain through the NASA technical library via You Tube media. The U.S. spacesuit KC topics have included lessons learned from some of the most prominent spacesuit experts and spacesuit users including current and former astronauts. The events have enriched the spacesuit legacy knowledge from Gemini, Apollo, Skylab, Space Shuttle and International Space Station Programs. As well, expert engineers and scientists have shared their challenges and successes to be remembered. The last few years have been some of the most successful years of the KC program program's life with numerous recordings and releases to the public. It is evidenced by the thousands that have view the recordings online. This paper reviews the events accomplished and archived over Fiscal Years 2012 and 2013 and highlights a few of the most memorable ones. This paper also communicates ways to access the events that are available internally to NASA as well as in the public domain.

Chullen, Cinda↗

NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗

DOE OSTI2021

CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. Here, we introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Evidence That Sharp Interfaces Suppress Recombination in Thick Organic Solar Cells

Commercialization and scale-up of organic solar cells (OSCs) using industrial solution printing requires maintaining maximum performance at active-layer thicknesses >400 nm – a characteristic still not generally achieved in non-fullerene acceptor OSCs. NT812:PC71BM is a rare system whose performance increases up to these thicknesses due to highly suppressed charge recombination relative to the classic Langevin model. The suppression in this system, however, uniquely depends on device processing, pointing towards the role of nano-morphology. We investigate the morphological origins of this suppressed recombination by combining results from a suite of X-ray techniques. We are surprised to find that while all investigated devices are composed of pure, similarly aggregated nanodomains, Langevin reduction factors can still be tuned from ~2 to >1000. This indicates that pure aggregated phases are insufficient for non-Langevin (reduced) recombination. Instead, we find that large well-ordered conduits and, in particular, sharp interfaces between domains appear to help to keep opposite charges separated and percolation pathways clear for enhanced charge collection in thick active layers. To our knowledge, this is the first quantitative study to isolate donor/acceptor interfacial width correlated to non-Langevin charge recombination. Furthermore, this new structure-property relationship will be key to successful commercialization of printed OSCs at scale.

14 SOLAR ENERGY↗

DOE OSTI2023

Summary report of the 4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis (FDPVA)

The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating 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 crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Summary of the 5th IAEA technical meeting on fusion data processing, validation and analysis (FDPVA)

The purpose of the 5th International Atomic Energy Agency technical meeting on fusion data processing, validation and analysis (FDPVA) (Ghent University, Ghent, Belgium, 12–15 June 2023) was to provide a platform during which a set of topics relevant to FDPVA were discussed with the view of meeting the needs of next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics data analysis and synthetic diagnostics reported at the meeting, including concept description of new devices; fusion databases; integrated data analysis; inverse problems; uncertainty propagation, verification and validation; probabilistic methods and machine learning. The relevant results underline trends observed in the current major fusion confinement devices.

fusion databases↗

DOE OSTI2025

Apple Bitter Rot: Biology, Ecology, Omics, Virulence Factors, and Management of Causal Colletotrichum Species

ABSTRACT Apple bitter rot is caused by various Colletotrichum spp. that threaten apple production globally resulting in millions of dollars in damage annually. The fungus causes a decline in fruit quality and yield, eventually rotting the fruit and rendering it inedible. The pathogen is difficult to keep out of orchards because of its broad host range and transmissibility by rain splash and insects. Once the disease manifests, pathogen identification is difficult due to evolving taxonomy and similar morphology between species. Current management strategies are threatened by an increase in fungicide resistance and regulations on many multisite fungicides, leading to a pressing need for new management options for control. This review aims to summarise the most current knowledge regarding the biology, virulence factors, ecology, omics and emerging management strategies for Colletotrichum species that cause apple bitter rot. Taxonomy Colletotrichum species—Domain Eukaryota, Kingdom Fungi, Phylum Ascomycota, Class Sordariomycetes, Order Glomerellales, Family Glomerellaceae, Genus Colletotrichum . Biology Hemibiotrophic pathogen with a wide host range that establishes a biotrophic interaction where it penetrates host plants using appressoria followed by a switch to necrotrophy causing rot symptoms. Toxins Cercosporin, colletotrichins, colletotric acid, ferricrocin. Host Range The host range varies by species but largely occurs on dicotyledonous plants and is less prevalent on monocots as well as gymnosperms, ferns, mosses and animals (e.g., insects). Disease Symptoms Symptoms often manifest as flat to sunken necrotic areas on fruit. Lesions on leaves and fruit can have concentric rings with abundant pathogen sporulation. Disease Control Colletotrichum spp. are primarily managed by single‐site quinone outside inhibitor (Qol), methyl benzimidazole carbamate (MBC), demethylation inhibitor (DMI) fungicides, and multisite dithiocarbamate and phthalimide fungicides. Susceptibility may vary with species, strain specificity, or geographic region. Other management options include clean stock production, cultural practices, resistance breeding, and biological control through the introduction of protective or competing microorganisms.

Boeckman, Nathanial J. [Plant Pathology Laboratory↗

DOE OSTI2024

Using Generative AI to implement the discrepancy checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough in AI. These developments are rapidly accelerating developments in different science and engineering applications. Nearly Autonomous Management and Control (NAMAC) system provides recommendations to the operator for maintaining the safety and performance of the reactor. The discrepancy checker (DC) is an important component of the NAMAC) system, whose goal is to determine if the plant is moving towards the expected system state after the control actions are injected. In this work, we explore generative AI methods, particularly, a generative pretrained transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert the operator in situations outside NAMAC’s scope and act as a chatbot the operator can use to retrieve relevant information. This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs are trained on huge amounts of undisclosed general domain datasets. We explored two methods to adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A small knowledge base (information file) that encompasses rules for DC implementation and some general information related to NAMAC has been created to support DC implementation using GPT. In this work, the GPT-based DC implementations have been tested for their reasoning abilities, comprehension, information retrieval, and extraction abilities. It should be noted that this paper only presents a preliminary study to test the feasibility of DC implementation using generative AI technology. Given the potential risks and severe consequences associated with nuclear reactor applications, combined with the black-box nature of AI, extensive offline and online testing and reliability analyses of GPT-based DCs are needed for further developing such capabilities.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation

We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

58 GEOSCIENCES↗

NASA NTRS1989

Intelligent situation assessment and response aiding in flight emergencies

A knowledge-based pilot aiding system which performs situation assessment and response aiding is described. The system uses a causal model of the flight domain to both simulate the effects of identified failures on flight and to derive responses during emergencies. The model represents information at two levels of abstraction: Boolean, which simply states whether a subsystem or aircraft component is normal or abnormal, and qualitative, which expresses the subsystem or component status as one of several qualitative values, such as increasing, decreasing, or stable.

Hudlicka, Eva↗

NASA NTRS1991

Generating target system specifications from a domain model using CLIPS

The quest for reuse in software engineering is still being pursued and researchers are actively investigating the domain modeling approach to software construction. There are several domain modeling efforts reported in the literature and they all agree that the components that are generated from domain modeling are more conducive to reuse. Once a domain model is created, several target systems can be generated by tailoring the domain model or by evolving the domain model and then tailoring it according to the specified requirements. This paper presents the Evolutionary Domain Life Cycle (EDLC) paradigm in which a domain model is created using multiple views, namely, aggregation hierarchy, generalization/specialization hierarchies, object communication diagrams and state transition diagrams. The architecture of the Knowledge Based Requirements Elicitation Tool (KBRET) which is used to generate target system specifications is also presented. The preliminary version of KBRET is implemented in the C Language Integrated Production System (CLIPS).

Sugumaran, Vijayan↗

NASA NTRS1993

A path-oriented matrix-based knowledge representation system

Experience has shown that designing a good representation is often the key to turning hard problems into simple ones. Most AI (Artificial Intelligence) search/representation techniques are oriented toward an infinite domain of objects and arbitrary relations among them. In reality much of what needs to be represented in AI can be expressed using a finite domain and unary or binary predicates. Well-known vector- and matrix-based representations can efficiently represent finite domains and unary/binary predicates, and allow effective extraction of path information by generalized transitive closure/path matrix computations. In order to avoid space limitations a set of abstract sparse matrix data types was developed along with a set of operations on them. This representation forms the basis of an intelligent information system for representing and manipulating relational data.

Feyock, Stefan↗

NASA NTRS1993

Learning Search Control Knowledge for Deep Space Network Scheduling

While the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, for specific distributions of problems and constraints, domain-specific solutions have been shown to perform in much better than exponential time.

Deep Space Network Search Control↗

NASA NTRS1999

Developing an Advanced Environment for Collaborative Computing

Knowledge management in general tries to organize and make available important know-how, whenever and where ever is needed. Today, organizations rely on decision-makers to produce "mission critical" decisions that am based on inputs from multiple domains. The ideal decision-maker has a profound understanding of specific domains that influence the decision-making process coupled with the experience that allows them to act quickly and decisively on the information. In addition, learning companies benefit by not repeating costly mistakes, and by reducing time-to-market in Research & Development projects. Group-decision making tools can help companies make better decisions by capturing the knowledge from groups of experts. Furthermore, companies that capture their customers preferences can improve their customer service, which translates to larger profits. Therefore collaborative computing provides a common communication space, improves sharing of knowledge, provides a mechanism for real-time feedback on the tasks being performed, helps to optimize processes, and results in a centralized knowledge warehouse. This paper presents the research directions. of a project which seeks to augment an advanced collaborative web-based environment called Postdoc, with workflow capabilities. Postdoc is a "government-off-the-shelf" document management software developed at NASA-Ames Research Center (ARC).

Becerra-Fernandez, Irma↗