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DOE OSTI2025

SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combine symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: (i) G-SympGNN and (ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. Finally, we also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

Deep learning↗

NASA NTRS2009

Surface Map Traffic Intent Displays and Net-Centric Data-link Communications for NextGen

By 2025, U.S. air traffic is predicted to increase three fold and may strain the current air traffic management system, which may not be able to accommodate this growth. In response to this challenge, a revolutionary new concept has been proposed for U.S. aviation operations, termed the Next Generation Air Transportation System or "NextGen". Many key capabilities are being identified to enable NextGen, including the use of data-link communications. Because NextGen represents a radically different approach to air traffic management and requires a dramatic shift in the tasks, roles, and responsibilities for the flight deck, there are numerous research issues and challenges that must be overcome to ensure a safe, sustainable air transportation system. Flight deck display and crew-vehicle interaction concepts are being developed that proactively investigate and overcome potential technology and safety barriers that might otherwise constrain the full realization of NextGen. The paper describes simulation research, conducted at National Aeronautics and Space Administration (NASA) Langley Research Center, examining data-link communications and traffic intent data during envisioned four-dimensional trajectory (4DT)-based and equivalent visual (EV) surface operations. Overall, the results suggest that controller pilot data-link communications (CPDLC) with the use of mandatory pilot read-back of all clearances significantly enhanced situation awareness for 4DT and EV surface operations. The depiction of graphical traffic state and intent information on the surface map display further enhanced off-nominal detection and pilot qualitative reports of safety and awareness.

Shelton, Kevin J.↗

NASA NTRS1972

Analysis of radiation and meteoroid satellite data

The data obtained in earth orbit by the Radiation and Meteoroid Satellite (RMS) were interpreted and reduced to a form which will be usable by future space experimenters. The required tasks are detailed. Computer programs were written which lifted the raw data and associated emphemeris data from the GFE magnetic tapes. The engineering data was then used to evaluate the performance of the spacecraft and the experiments. The radiation data was used to prepare flux, spectral, and dose maps of the South Atlantic magnetic anomaly where possible. The meteoroid data was used to determine a rough estimate of the meteoroid flux and in general evaluate the performance of the thin-film meteoroid sensors. The degree of success of the RMS mission was evaluated in light of the separation anomaly which occurred between RMS and OFO during launch.

Farmer, B. J.↗

NASA NTRS1985

Electro-science Requirements for Shuttle-attached Antenna Flight Experiments

The activities of an in-house electro-science task group organized to conduct studies of shuttle-attached flight experiments using the 15-meter hoop-column antennas as a research tool for developing both improved sensor technology and LSA technology are described. Some experiments could provide significant amounts of scientific data such as radio star mapping and definition of ocean current eddies over limited geographic regions. The experiments originate from the microwave remote sensing community and other areas which require the inherently higher resolution and boresite gain of large space antennas. Technology experiments are also being studied which would use the 15-meter antenna experiments as a stepping stone to 50 to 100 meter class reflector technology in the future. An antenna technology experiment using the 15-meter antenna in a shuttle-attached mission is discussed. Electromagnetic modeling is correct for each major subsystem and also to verify the interrelations of the subsystems.

Grantham, W. L.↗

NASA NTRS1989

A fast lightstripe rangefinding system with smart VLSI sensor

The focus of the research is to build a compact, high performance lightstripe rangefinder using a Very Large Scale Integration (VLSI) smart photosensor array. Rangefinding, the measurement of the three-dimensional profile of an object or scene, is a critical component for many robotic applications, and therefore many techniques were developed. Of these, lightstripe rangefinding is one of the most widely used and reliable techniques available. Though practical, the speed of sampling range data by the conventional light stripe technique is severely limited. A conventional light stripe rangefinder operates in a step-and-repeat manner. A stripe source is projected on an object, a video image is acquired, range data is extracted from the image, the stripe is stepped, and the process repeats. Range acquisition is limited by the time needed to grab the video images, increasing linearly with the desired horizontal resolution. During the acquisition of a range image, the objects in the scene being scanned must be stationary. Thus, the long scene sampling time of step-and-repeat rangefinders limits their application. The fast range sensor proposed is based on the modification of this basic lightstripe ranging technique in a manner described by Sato and Kida. This technique does not require a sampling of images at various stripe positions to build a range map. Rather, an entire range image is acquired in parallel while the stripe source is swept continuously across the scene. Total time to acquire the range image data is independent of the range map resolution. The target rangefinding system will acquire 1,000 100 x 100 point range images per second with 0.5 percent range accuracy. It will be compact and rugged enough to be mounted on the end effector of a robot arm to aid in object manipulation and assembly tasks.

Gruss, Andrew↗

NASA NTRS1994

A study of a multi-pinned phase CCD detector for use as a star tracker

This grant has supported studies of the use of charge coupled devices (CCD's) in star trackers in a number of different areas. Among the tasks we have pursued are the following: (1) radiation modeling and the increase in noise equivalent angle as a result of exposure to protons and bremsstrahlung gamma rays; (2) the development of pattern matching software to identify field locations from a CCD image and a pre-existing map of the local area; (3) observations of various stellar fields from the 24 inch telescope at the Offit Observatory at JHU, primarily to test the pattern matching software (these included crowded fields as well as moving objects, like comets); and (4) tracking of very faint objects to determine the faint limit of the CCD system.

Friedman, Scott D.↗

NASA NTRS2002

Stirling Research Laboratory Providing Independent Performance Verification of Convertors for a Stirling Radioisotope Generator

The Department of Energy (DOE), Germantown, Maryland, Stirling Technology Company (STC), Kennewick, Washington, and NASA Glenn Research Center are developing a free-piston Stirling convertor for a high-efficiency Stirling Radioisotope Generator for NASA Space Science missions. This generator is being developed for multimission use, including providing electric power for unmanned Mars rovers and for deep space missions. STC is developing the 55-W Technology Demonstration Convertor (TDC) under contract to DOE. Glenn is conducting an in-house technology project to assist in developing the convertor for readiness for space qualification and mission implementation. As part of this effort, a Stirling Research Laboratory was established to test the TDC's and related technologies. A key task is providing an independent verification and validation of the TDC performance. Four TDC's are now being tested at Glenn. Acceptance testing has been completed for all convertors, and in general, performance agreed well with that achieved by STC prior to the delivery of the convertors. Performance mapping has also been completed on two of the convertors over a range of hot-end temperatures (450 to 650 C), cold-end temperatures (80 to 120 C), and piston amplitudes (5.2 to 6.2 mm). These test data are available online at http://www.grc.nasa.gov/WWW/tmsb/. The TDC's can be tested in either a horizontal orientation with dual-opposed convertors or in a vertical orientation with a single convertor. Synchronized dual-opposed pairs are used for dynamically balanced operation that results in very low levels of vibration. The Stirling Research Laboratory also supports launch environment testing of the TDC's in Glenn's Structural Dynamics Laboratory and electromagnetic interference and electromagnetic compatibility characterization and reduction efforts. In addition, the TDC's will be used for long-term endurance testing, and preparations are underway for unattended operation.

Thieme, Lanny G.↗

DOE OSTI2019

Tau covariance patterns in Alzheimer's disease patients match intrinsic connectivity networks in the healthy brain

According to the network model of neurodegeneration, the spread of pathogenic proteins occurs selectively along connected brain regions. We tested in vivo whether the distribution of filamentous tau (measured with [ 18 F]flortaucipir-PET), fibrillar amyloid-β ([ 11 C]PIB-PET) and glucose hypometabolism ([ 18 F]FDG-PET) follows the intrinsic functional organization of the healthy brain. We included 63 patients with Alzheimer's disease (AD; 30 male, 63 ± 8 years) who underwent [ 18 F]flortaucipir, [ 11 C]PIB and [ 18 F]FDG PET, and 1000 young adults (427 male, 21 ± 3 years) who underwent task-free fMRI. We selected six predefined disease epicenters as seeds for whole-brain voxelwise covariance analyses to compare correlated patterns of tracer uptake across AD patients against fMRI intrinsic connectivity patterns in young adults. We found a striking convergence between [18F]flortaucipir covariance patterns and intrinsic connectivity maps (range Spearman rho's: 0.32-0.78, p < .001), which corresponded with expected functional networks (range goodness-of-fit: 3.8-8.2). The topography of amyloid-β covariance patterns was more diffuse and less network-specific, while glucose hypometabolic patterns were more spatially restricted than tau but overlapped with functional networks. These findings suggest that the spatial patterns of tau and glucose hypometabolism observed in AD resemble the functional organization of the healthy brain, supporting the notion that tau pathology spreads through circumscribed brain networks and drives neurodegeneration.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1979

Evaluation and Analysis of SEASAT-A Scanning Multichannel Microwave Radiometer (SSMR) Antenna Pattern Correction (APC) Algorithm. Sub-task 4: Interim Mode T Sub B Versus Cross and Nominal Mode T Sub B

The brightness temperature data produced by the SMMR Antenna Pattern Correction algorithm are evaluated. The evaluation consists of: (1) a direct comparison of the outputs of the interim, cross, and nominal APC modes; (2) a refinement of the previously determined cos beta estimates; and (3) a comparison of the world brightness temperature (T sub B) map with actual SMMR measurements.

Kitzis, J. L.↗

NASA NTRS1989

Exploiting map plans as resources for action

When plans are used as programs for controlling the action of autonomous or teleoperated robots, their abstract representation can easily obscure a great deal of the critical knowledge that originally led to the planned course of action. An autonomous vehicle experiment is highlighted which illustrates how the information barriers created by abstraction can result in undesirable action. It is then shown how the same task can be performed correctly using plans as a resource for action. As a result of this simple change in outlook, problems requiring opportunistic reaction to unexpected changes in the environment can be solved.

Payton, David↗

NASA NTRS2005

NASA Tech Briefs, December 2005

Topics covered include: Video Mosaicking for Inspection of Gas Pipelines; Shuttle-Data-Tape XML Translator; Highly Reliable, High-Speed, Unidirectional Serial Data Links; Data-Analysis System for Entry, Descent, and Landing; Hybrid UV Imager Containing Face-Up AlGaN/GaN Photodiodes; Multiple Embedded Processors for Fault-Tolerant Computing; Hybrid Power Management; Magnetometer Based on Optoelectronic Microwave Oscillator; Program Predicts Time Courses of Human/ Computer Interactions; Chimera Grid Tools; Astronomer's Proposal Tool; Conservative Patch Algorithm and Mesh Sequencing for PAB3D; Fitting Nonlinear Curves by Use of Optimization Techniques; Tool for Viewing Faults Under Terrain; Automated Synthesis of Long Communication Delays for Testing; Solving Nonlinear Euler Equations With Arbitrary Accuracy; Self-Organizing-Map Program for Analyzing Multivariate Data; Tool for Sizing Analysis of the Advanced Life Support System; Control Software for a High-Performance Telerobot; Java Radar Analysis Tool; Architecture for Verifiable Software; Tool for Ranking Research Options; Enhanced, Partially Redundant Emergency Notification System; Close-Call Action Log Form; Task Description Language; Improved Small-Particle Powders for Plasma Spraying; Bonding-Compatible Corrosion Inhibitor for Rinsing Metals; Wipes, Coatings, and Patches for Detecting Hydrazines; Rotating Vessels for Growing Protein Crystals; Oscillating-Linear-Drive Vacuum Compressor for CO2; Mechanically Biased, Hinged Pairs of Piezoelectric Benders; Apparatus for Precise Indium-Bump Bonding of Microchips; Radiation Dosimetry via Automated Fluorescence Microscopy; Multistage Magnetic Separator of Cells and Proteins; Elastic-Tether Suits for Artificial Gravity and Exercise; Multichannel Brain-Signal-Amplifying and Digitizing System; Ester-Based Electrolytes for Low-Temperature Li-Ion Cells; Hygrometer for Detecting Water in Partially Enclosed Volumes; Radio-Frequency Plasma Cleaning of a Penning Malmberg Trap; Reduction of Flap Side Edge Noise - the Blowing Flap; and Preventing Accidental Ignition of Upper-Stage Rocket Motors.

Source record↗

DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗

DOE OSTI2023

Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Simulation is crucial for all aspects of collider data analysis, but the available computing budget in the High Luminosity LHC era will be severely constrained. Generative machine learning models may act as surrogates to replace physics-based full simulation of particle detectors, and diffusion models have recently emerged as the state of the art for other generative tasks. Here, we introduce CaloDiffusion, a denoising diffusion model trained on the public CaloChallenge datasets to generate calorimeter showers. Our algorithm employs 3D cylindrical convolutions, which take advantage of symmetries of the underlying data representation. To handle irregular detector geometries, we augment the diffusion model with a new geometry latent mapping (GLaM) layer to learn forward and reverse transformations to a regular geometry that is suitable for cylindrical convolutions. The showers generated by our approach are nearly indistinguishable from the full simulation, as measured by several different metrics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1992

Cortical localization of cognitive function by regression of performance on event-related potentials

This paper demonstrates a new method of mapping cortical localization of cognitive function, using electroencephalographic data. Cross-subject regression analyses are used to identify cortical sites and post-stimulus latencies where there is a high correlation between subjects' performance and their cognitive event-related potential amplitude. The procedure was tested using a mental arithmetic task and was found to identify essentially the same cortical regions that have been associated with such tasks on the basis of research with patients suffering localized cortical lesions. Thus, it appears to offer an inexpensive, noninvasive tool for exploring the dynamics of localization in neurologically normal subjects.

Montgomery, R. W.↗

NASA NTRS2003

Application of Satellite SAR Imagery in Mapping the Active Layer of Arctic Permafrost

The objective of this project is to map the spatial variation of the active layer over the arctic permafrost in terms of two parameters: (i) timing and duration of thaw period and (ii) differential frost heave and thaw settlement of the active layer. To achieve this goal, remote sensing, numerical modeling, and related field measurements are required. Tasks for the University of Colorado team are to: (i) determine the timing of snow disappearance in spring through changes in surface albedo (ii) simulate the freezing and thawing processes of the active layer and (iii) simulate the impact of snow cover on permafrost presence.

Zhang, Ting-Jun↗

NASA NTRS2005

Apex Reference Manual 3.0 Beta

Apex is a toolkit for constructing software that behaves intelligently and responsively in demanding task environments. Reflecting its origin at NASA where Apex continues to be developed, current applications include: a) Providing autonomous mission management and tactical control capabilities for unmanned aerial vehicles including an autonomous surveillance helicopter and a simulation prototype of an unmanned fixed-wing aircraft to be used for wildfire mapping; b) Simulating human air traffic controllers, pilots and astronauts to help predict how people might respond to changes in equipment or procedures; and c) Predicting the precise duration and sequence of routine human behaviors based on a human-computer interaction engineering technique called CPM-GOMS. Among Apex s components are a set of implemented reasoning services, such as those for reactive planning and temporal pattern recognition; a software architecture that embeds and integrates these services and allows additional reasoning elements to be added as extensions; a formal language for specifying agent knowledge; a simulation environment to facilitate prototyping and analysis; and Sherpa, a set of tools for visualizing autonomy logic and runtime behavior. In combination, these are meant to provide a flexible and usable framework for creating, testing, and deploying intelligent agent software. Overall, our goal in developing Apex is to lower economic barriers to developing intelligent software agents. New ideas about how to extend or modify the system are evaluated in terms of their impact in reducing the time, expertise, and inventiveness required to build and maintain applications. For example, potential enhancements to the AI reasoning capabilities in the system are reviewed not only for usefulness and distinctiveness, but also for their impact on the readability and general usability of Apex s behavior representation language (PDL) and on the transparency of resulting behavior. A second central part of our approach is to iteratively refine Apex based on lessons learned from as diverse a set of applications as possible. Many applications have been developed by users outside the core development team including engineers, researchers, and students. Usability is thus a central concern for every aspect of Apex visible to a user, including PDL, Sherpa, the Apex installation process, APIs, and user documentation. Apex users vary in their areas of expertise and in their familiarity with autonomy technology. Focusing on usability, a development philosophy summarized by the project motto "Usable Autonomy," has been important part of enabling diverse users to employ Apex successfully and to provide feedback needed to guide iterative, user-centered refinement.

Freed, Michael A.↗

DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Generating accurate density matrices on the tangent space of a Grassmann manifold

Interpolating a density matrix from a set of known density matrices is not a trivial task. This is because a linear combination of density matrices does not necessarily correspond to another density matrix. In this Communication, density matrices are examined as objects of a Grassmann manifold. Although this manifold is not a vector space, its tangent space is a vector space. As a result, one can map the density matrices on this manifold to their corresponding vectors in the tangent space and then perform interpolations on that tangent space. The resulting interpolated vector can be mapped back to the Grassmann manifold, which can then be utilized (1) as an optimal initial guess for a self-consistent field (SCF) calculation or (2) to derive energy directly without time-consuming SCF iterations. Such a promising approach is denoted as Grassmann interpolation (G-Int). The hydrogen molecule has been used to illustrate that the described interpolated method in this work preserves the essential attributes of a density matrix. For phosphorus mononitride and ferrocene, it was demonstrated numerically that reference points for the definition of the corresponding tangent spaces can be chosen arbitrarily. In addition, the interpolated density matrices provide a superior and essentially converged initial guess for an SCF calculation to make the SCF procedure itself unnecessary. Finally, this accurate, efficient, robust, and systematically improved G-Int strategy has been used for the first time to generate highly accurate potential energy surfaces with fine details for the difficult case, ferrocene.

Chemistry↗