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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Origins of the odd optical observables in plutonium and americium tungstates

A series of trivalent f-block tungstates, MW 2 O 7 (OH)(H 2 O) (M = La, Ce, Pr, Nd, and Pu) and AmWO 4 (OH), have been prepared in crystalline form using hydrothermal methods. Both structure types take the form of 3D networks where MW 2 O 7 (OH)(H 2 O) is assembled from infinite chains of distorted tungstate octahedra linked by isolated MO 8 bicapped trigonal prisms; whereas AmWO 4 (OH) is constructed from edge-sharing AmO 8 square antiprisms connected by distorted tungstate trigonal bipyramids. PuW 2 O 7 (OH)(H 2 O) crystallizes as red plates; an atypical color for a Pu( III ) compound. Optical absorption spectra acquired from single crystals show strong, broadband absorption in the visible region. A similar feature is observed for CeW 2 O 7 (OH)(H 2 O), but not for AmWO 4 (OH). Here we demonstrate that these significantly different optical properties do not stem directly from the 5f electrons, as in both systems the valence band has mostly O-2p character and the conduction band has mostly W-5d character. Furthermore, the quasi-particle gap is essentially unaffected by the 5f degrees of freedom. Despite this, our analysis demonstrates that the f-electron covalency effects are quite important and substantially different energetically in PuW 2 O 7 (OH)(H 2 O) and AmWO 4 (OH), indicating that the optical gap alone cannot be used to infer conclusions concerning the f electron contribution to the chemical bond in these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Automatic Generation of High-Performance Convolution Kernels on ARM CPUs for Deep Learning

In this work, we present FastConv, a template-based code auto-generation open source library that can automatically generate high-performance deep learning convolution kernels of arbitrary matrices/tensors shapes. FastConv is based on the Winograd algorithm, which is reportedly the highest performing algorithm for the time-consuming convolution layers of convolutional neural networks. ARM CPUs cover a wide range designs and specifications, from embedded devices to HPC-grade CPUs. The leads to the dilemma of how to consistently optimize Winograd-based convolution solvers for convolution layers of different shapes. FastConv addresses this problem by using templates to auto-generate multiple shapes of tuned kernels variants suitable for skinny tall matrices. As a performance portable library, FastConv transparently searches for the best combination of kernel shapes, cache tiles, scheduling of loop orders, packing strategies, access patterns, and online/offline computations. Auto-tuning is used to search the parameter configuration space for the best performance for a given target architecture and problem size. The experiments with layer-wise evaluation on the VGG--16 model confirms a 1.25x performance gains is got by tuning the Winograd library. Integrated comparison results shows 1.02x to 1.40x, 1.14x to 2.17x, and 1.22x and 2.48x speedup is achieved over NNPACK, Arm NN, and FeatherCNN on the Kunpeng 920 beside few cases. Furthermore, problem size performance portability experiments with various convolution shapes shows that FastConv achieves 1.2x to 1.7x speedup and 2x to 22x speedup over NNPACK and ARM NN inference engine using Winograd on Kunpeng 920 . CPU performance portability evaluation on the VGG--16 show an average speedup over NNPACK of 1.42x, 1.21x, 1.26x, 1.37x, 2.26x, and 11.02x is observed on Kunpeng 920, Snapdragon 835, 855, 888, Apple M1, and AWS Graviton2, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Geometry-aware framework for deep energy method: An application to structural mechanics with hyperelastic materials

Here, in this work, we introduce a novel physics-informed framework named the Geometry-Aware Deep Energy Method (GADEM) for solving structural mechanics problems on different geometries. As the weak form of the physical system equation (or the energy-based approach) has demonstrated clear advantages compared to the strong form for solving solid mechanics problems, GADEM employs the weak form and aims to infer the solution on multiple shapes of geometries. Integrating a geometry-aware framework into an energy-based method results in an effective physics-informed deep learning model in terms of accuracy and computational cost. Different ways to represent the geometric information and to encode the geometric latent vectors are investigated in this work. We introduce a loss function of GADEM which is minimized based on the potential energy of all considered geometries. An adaptive learning method is also employed for the sampling of collocation points to enhance the performance of GADEM. We present some applications of GADEM to solve solid mechanics problems, including a loading simulation of a toy tire involving contact mechanics and large deformation hyperelasticity. The numerical results of this work demonstrate the remarkable capability of GADEM to infer the solution on various and new shapes of geometries using only one trained model.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Temporal Evolution of SL-9 Impact Sites on Jupiter and Global Maps of Jupiter from Multi-Observatory Visible and Infrared Images

The objective of this research was to investigate the temporal behavior of the impact features on Jupiter created by the fragments of the Shoemaker Levy-9 comet that collided with the planet in July 1994. The primary observations used in the study were ground based images of Jupiter acquired from the Swedish Solar Vacuum Tube on the island of La Palma in the Canary Islands. The measurement of position of the impact features in images acquired immediately after the impact over a period of a few days revealed that the apparent drift rates were too high and that a repetitive pattern could be seen in the longitude position on successive rotations. This could be explained only by the fact that the measured longitudes of the impact sites were being affected by parallax due to a significant elevation of the impact debris above the nominal cloud top altitude value used for image navigation. Once the apparent positions are analyzed as a function of the meridian angle, the parallax equation can be used to infer the height of the impact features above the cloud deck, once the true impact position (longitude) for the feature is known. Due to their inherent high spatial resolution, the HST measurements of the impact site locations have been accepted widely. However, these suffer from the parallax themselves since few of them were obtained at central meridian. Ground based imaging have the potential to improve this knowledge as they do observe most of the impact sites on either side of the central meridian, except for the degraded resolution. Measurements over a large number of images enables us to minimize the position error through regression and thus estimate both the actual impact site location devoid of parallax bias, and also of the altitude level of the impact debris above the cloud deck. With rapid imaging there is the potential to examine the time evolution of the altitude level. Several hundred ground based images were processed, navigated and subjected to the impact site location measurements. HST images were also acquired and used to calibrate the results and to improve the sample. The resources available enabled an in-depth study only of impact site A, however, many more images have since become available through the global network observations through Lowell Observatory.

Limaye, Sanjay S.↗
DOE OSTI2018

Advanced Analytics Studies Applied to US Department of Veterans Affairs' Corporate Data Warehouse (Initial Draft)

In this report, we describe our experience of applying several advanced analytics algorithms to the US Department of Veterans Affairs’ (VA’s) Corporate Data Warehouse (CDW) electronic health records datasets during FY2017-18. While various algorithms were applied to the CDW data, the main goal of this effort was to provide useful insights into the operational aspects (as opposed to the purely clinical aspect) of this specific implementation. Since there are not many reports in the public literature on advanced analytics applied to this data, this report provides unique insight in this regard. We focused on two machine learning (ML) applications: (1) clinical pathway inference (patterns dominating clinical pathways applied to a male dataset of Stable Ischemic Heart Disease patients as use case) and (2) medical-concept representation learning’s capability to inform and refine cohort membership based on probable patient outcomes. During our study on clinical pathway inference, we used two main modeling techniques: (a) topic probabilistic modeling (latent Dirichlet allocation [LDA]) and (b) feature imbedding. We observed that the applicability of LDA to the pathway inference remains in question. LDA provides results that are intuitive and easy for humans to comprehend; however, when applied to the pathway inference, it also generates pathway patterns that consist of a few dominant pathway components. In addition, LDA provides no information regarding the relationships among components in the same pathway. The outputs of LDA are also found to categorize patients based on their trace data of clinical procedures. However, the results suggest that LDA is biased toward statistically dominant components. This makes it particularly hard to discriminate pathway subbranches. To address the issue, we designed a new pathway inference methodology that integrates temporal ordering of pathway components into LDA results, as existing feature embedding tools such as word2vec are not readily applicable to our task. In addition, we developed our own feature embedding tool customized for patient traces. We conclude this study by suggesting two approaches to use embedding representation of a component into LDA and briefly list our lessons learned and recommendations for future work. For our study on medical-concept representation learning ability to inform and refine cohort membership based on probable patient outcomes, during our empirical evaluation we compared methods from medical-concept learning to standard one of a kind (one-of-K) encoding to evaluate the change in effectiveness as done in Choi, Schuetz, Stewart, and Sun (2016). We performed two primary empirical evaluations. The first evaluation was on a curated collection of 60,000 patients with no more than 1 year of medical history included. The second was a collection of patients with no restriction to the amount of medical history included. The hypothesis across both evaluations is that representation learning is broadly useful independent of downstream processing models; thus, for each experiment, we trained three models—a logistic regression model, a two-layer neural network model, and a nearest-neighbor model—and then averaged the results. Each model was trained using fivefold cross validation. We provided evidence that medical-representation learning improves predictions of the primary diagnosis category of a short patient history through 12 experiments. In addition, we provided evidence that medical-representation learning fails to improve prediction of the primary diagnosis category of an arbitrarily long patient history through 12 experiments. We conclude this study with a brief list of our lessons learned and recommendations for future work. We conclude the report with a discussion of recent technology trends as they relate to our artificial intelligence and, more specifically, to our ML research and approaches as described previously.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine learning enhanced predictions of ICRF heating: Overcoming numerical limitations via data curation

In this work, we present the development of robust surrogate models for Ion Cyclotron Range of Frequencies (ICRF) and High-Harmonic Fast Wave (HHFW) heating predictions in fusion plasmas. Building upon our previous efforts to achieve real-time capable models, we identify the cause of the outliers found using TORIC in certain HHFW heating scenarios. The outliers are observed to be spurious ion Bernstein wave (IBW)-like modes caused by a wavelength control algorithm designed to address challenging scenarios with high perpendicular wavenumbers. The effect arises from the modulation in the perpendicular susceptibility, which can induce sign reversal and IBW-like propagation for scenarios featuring normalized ion Larmor radius λ i ≫ 1. We use TORIC with this algorithm disabled to generate a novel HHFW-NSTX database that is free of outliers. Surrogate models trained on this database, including Random Forest Regressor (RFR), Multi-Layer Perceptrons, and Gaussian Process Regressors (GPR), demonstrate the ability to accurately predict HHFW heating profiles, with regression scores of R 2 ∈[0.93−0.99]. Additionally we demonstrate that it is possible to generalize predictions beyond training data by the use of both RFR and GPR models, enabling the prediction of scenarios previously limited to the original model. GPR models also provide uncertainty quantification, offering insights into model confidence. This work introduces a comprehensive Verification, Validation, and Uncertainty Quantification methodology for surrogate modeling, applicable not only to ICRF heating but also to other RF heating challenges and fusion physics problems. Beyond accelerated inference, these models show effective extrapolation capabilities, providing an alternative for addressing numerical challenges.

Artificial neural networks↗
DOE OSTI2013

The ultraluminous GRB 110918A

GRB 110918A is the brightest long gamma-ray burst (GRB) detected by Konus-WIND during its almost 19 yr of continuous observations and the most luminous GRB ever observed since the beginning of the cosmological era in 1997. We report on the final Interplanetary Network localization of this event and its detailed multiwavelength study with a number of space-based instruments. The prompt emission is characterized by a typical duration, a moderate peak energy of the time-integrated spectrum, and strong hard-to-soft evolution. The high observed energy fluence yields, at z = 0.984, a huge isotropic-equivalent energy release E {sub iso} = (2.1 ± 0.1) × 10{sup 54} erg. The record-breaking energy flux observed at the peak of the short, bright, hard initial pulse results in an unprecedented isotropic-equivalent luminosity L {sub iso} = (4.7 ± 0.2) × 10{sup 54} erg s{sup –1}. A tail of the soft γ-ray emission was detected with temporal and spectral behavior typical of that predicted by the synchrotron forward-shock model. The Swift/X-Ray Telescope and the Swift/Ultraviolet Optical Telescope observed the bright afterglow from 1.2 to 48 days after the burst and revealed no evidence of a jet break. The post-break scenario for the afterglow is preferred from our analysis, with a hard underlying electron spectrum and interstellar-medium-like circumburst environment implied. We conclude that, among the multiple reasons investigated, the tight collimation of the jet must have been a key ingredient to produce this unusually bright burst. The inferred jet opening angle of 1.°7-3.°4 results in reasonable values of the collimation-corrected radiated energy and the peak luminosity, which, however, are still at the top of their distributions for such tightly collimated events. We estimate a detection horizon for a similar ultraluminous GRB of z ∼ 7.5 for Konus-WIND and z ∼ 12 for the Swift/Burst Alert Telescope, which stresses the importance of GRBs as probes of the early Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Investigation of Scale-Dependent Groundwater/Surface-water Exchange in Rivers by Gradient Self-Potential Logging: Numerical Modeling and Field Experiments

Exchanges of groundwater and surface-water are fundamental to a wide range of water-supply and water-quality management issues but challenging to map beyond the reach scale. Waterborne gradient self-potential (SP) measurements are directly sensitive to water flow through riverbed sediments and can be used to infer exchange locations, direction (gain versus loss), scale, and relative changes, but to date applications to river corridor hydrology are limited. Numerical modeling and field experiments were therefore performed herein, each emphasizing waterborne gradient SP logging for identifying and locating focused vertical groundwater discharge (surface-water gain) and recharge (surface-water loss) in a river. Two and three-dimensional numerical models were constructed to simulate the polarities, appearances, and peak amplitudes of streaming-potential and electric-field anomalies on a riverbed and in the surface-water that were attributable to steady-state vertical fluxes of groundwater through high-permeability conduits in the riverbed. Effects of varied hydraulic length-scale of exchange and surface-water depth were tested through numerical modeling. Modeling results aided in data acquisition and interpretation for three repeated field experiments performed along a 1.5–2.0 km reach of the Quashnet River in Cape Cod, Massachusetts, where focused, meter-scale groundwater discharges occur at discrete locations within otherwise ubiquitous and more diffuse groundwater upwelling conditions. Strong gradient SP anomalies were repeatedly measured in the Quashnet River at previously confirmed locations of focused groundwater discharge, showing the efficacy of waterborne gradient SP logging in identifying and characterizing groundwater/surface water exchange dynamics at multiple river network scales.

Engineering↗
DOE OSTI2020

Seismic tomography of compressional wave velocity and attenuation structure for Makushin Volcano, Alaska

Located in the central portion of the Alaska-Aleutian subduction zone, Makushin Volcano is among the most active volcanoes in the United States and has been classified as a very high threat by the National Volcano Early Warning System based on its eruptive history and proximity to the City of Unalaska and international air routes. In 2015, five standalone seismic stations and three mini seismic arrays of 15 stations each were deployed on Unalaska Island to supplement the Alaska Volcano Observatory permanent seismic network. This temporary array was operational for one year. Taking advantage of the increased azimuthal coverage and the array's increased earthquake detection capability, we developed body-wave Vp and Vp/Vs seismic images of the velocity structure beneath the volcano. Our results show a complex structure with the upper 4 km of the crust. The shallow high-Vp features possibly delineate remnant magma pathways or conduits. Low-Vp regions are found east of the caldera at approximately 4–8 km depth. This is in agreement with previous seismic tomographic work and InSAR-based geodetic models, which had identified this region as a possible long-term source of magma. A three-dimensional compressional wave attenuation model complemented the velocity models. In general, we observe regions of corresponding low P-wave velocity and high attenuation east of the summit caldera between 5 and 7 km depth, supporting the presence of a melt-rich zone. The absolute Vp values (5.5–6.0 km/s) in this region are consistent with the presence of a magma body with a mafic signature. Here, we suggest that a triggering mechanism that involves mafic injections into a more evolved shallow magma chamber is representative of the current magmatic system below the volcano, similarly to what petrological studies have inferred for past eruptions.

58 GEOSCIENCES↗
DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
DOE OSTI2018

Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks

Real-time analysis of patient data during medical procedures can provide vital diagnostic feedback that significantly improves chances of success. With sensors becoming increasingly fast, frameworks such as Deep Neural Networks are required to perform calculations within the strict timing constraints for real-time operation. However, traditional computing platforms responsible for running these algorithms incur a large overhead due to communication protocols, memory accesses, and static (often generic) architectures. In this work, we implement a low-latency Multi-Layer Perceptron (MLP) processor using Field Programmable Gate Arrays (FPGAs). Unlike CPUs and Graphics Processing Units (GPUs), our FPGA-based design can directly interface sensors, storage devices, display devices and even actuators, thus reducing the delays of data movement between ports and compute pipelines. Moreover, the compute pipelines themselves are tailored specifically to the application, improving resource utilization and reducing idle cycles. We demonstrate the effectiveness of our approach using mass-spectrometry data sets for real-time cancer detection. We demonstrate that correct parameter sizing, based on the application, can reduce latency by 20% on average. Furthermore, we show that in an application with tightly coupled data-path and latency constraints, having a large amount of computing resources can actually reduce performance. Using mass-spectrometry benchmarks, we show that our proposed FPGA design outperforms both CPU and GPU implementations, with an average speedup of 144x and 21x, respectively. In our work, we demonstrate the importance of application-specific optimizations in order to minimize latency and maximize resource utilization for MLP inference. By directly interfacing and processing sensor data with ultra-low latency, FPGAs can perform real-time analysis during procedures and provide diagnostic feedback that can be critical to achieving higher percentages of successful patient outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Mechanism and dynamics of fatty acid photodecarboxylase

Photoenzymes are rare biocatalysts driven by absorption of a photon at each catalytic cycle; they inspire development of artificial photoenzymes with valuable activities. Fatty acid photodecarboxylase (FAP) is a natural photoenzyme that has potential applications in the bio-based production of hydrocarbons, yet its mechanism is far from fully understood. RATIONALE To elucidate the mechanism of FAP, we studied the wild-type (WT) enzyme from Chlorella variabilis (CvFAP) and variants with altered active-site residues using a wealth of techniques, including static and time-resolved crystallography and spectroscopy, as well as biochemical and computational approaches. RESULTS A 1.8-Å-resolution CvFAP x-ray crystal structure revealed a dense hydrogen-bonding network positioning the fatty acid carboxyl group in the vicinity of the flavin adenine dinucleotide (FAD) cofactor. Structures solved from free electron laser and low-dose synchrotron x-ray crystal data further highlighted an unusual bent shape of the oxidized flavin chromophore, and showed that the bending angle (14°) did not change upon photon absorption (step 1) or throughout the photocycle. Calculations showed that bending substantially affected the energy levels of the flavin. Structural and spectroscopic analysis of WT and mutant proteins targeting two conserved active-site residues, R451 and C432, demonstrated that both residues were crucial for proper positioning of the substrate and water molecules and for oxidation of the fatty acid carboxylate by 1 FAD* (~300 ps in WT FAP) to form FAD ∙– (step 2). Time-resolved infrared spectroscopy demonstrated that decarboxylation occured quasi-instantaneously upon this forward electron transfer, consistent with barrierless bond cleavage predicted by quantum chemistry calculations and with snapshots obtained by time-resolved crystallography. Transient absorption spectroscopy in H 2 O and D 2 O buffers indicated that back electron transfer from FAD ∙– was coupled to and limited by transfer of an exchangeable proton or hydrogen atom (step 3). Unexpectedly, concomitant with FAD ∙– reoxidation (to a red-shifted form FAD RS ) in 100 ns, most of the CO 2 product was converted, most likely into bicarbonate (as inferred from FTIR spectra of the cryotrapped FAD RS intermediate). Calculations indicated that this catalytic transformation involved an active-site water molecule. Cryo-Fourier transform infrared spectroscopy studies suggested that bicarbonate formation (step 4) was preceded by deprotonation of an arginine residue (step 3). At room temperature, the remaining CO 2 left the protein in 1.5 μs (step 4'). The observation of residual electron density close to C432 in electron density maps derived from time-resolved and cryocrystallography data suggests that this residue may play a role in stabilizing CO 2 and/or bicarbonate. Three routes for alkane formation were identified by quantum chemistry calculations; the one shown in the figure is favored by the ensemble of experimental data. CONCLUSION Finally, we provide a detailed and comprehensive characterization of light-driven hydrocarbon formation by FAP, which uses a remarkably complex mechanism including unique catalytic steps. We anticipate that our results will help to expand the green chemistry toolkit.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2012

Adaptive Sampling of Time Series During Remote Exploration

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Thompson, David R.↗
NASA NTRS2014

How Well Do Satellite AOD Observations Represent the Spatial and Temporal Variability of PM2.5 Concentration for the United States?

Due to their extensive spatial coverage, satellite Aerosol Optical Depth (AOD) observations have been widely used to estimate and predict surface PM2.5 concentrations. While most previous studies have focused on establishing relationships between collocated, hourly or daily AOD and PM2.5 measurements, in this study, we instead focus on the comparison of the large-scale spatial and temporal variability between satellite AOD and PM2.5 using monthly mean measurements. A newly developed spectral analysis technique e Combined Maximum Covariance Analysis (CMCA) is applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Ozone Monitoring Instrument (OMI) AOD datasets and Environmental Protection Agency (EPA) PM2.5 data, in order to extract and compare the dominant modes of variability. Results indicate that AOD and PM2.5 agree well in terms of interannual variability. An overall decrease is found in both AOD and PM2.5 across the United States, with the strongest signal over the eastern US. With respect to seasonality, good agreement is found only for Eastern US, while for Central and Western US, AOD and PM2.5 seasonal cycles are largely different or even reversed. These results are verified using Aerosol Robotic Network (AERONET) AOD observations and differences between satellite and AERONET are also examined. MODIS and MISR appear to have the best agreement with AERONET. In order to explain the disagreement between AOD and PM2.5 seasonality, we further use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) extinction profile data to investigate the effect of two possible contributing factors, namely aerosol vertical distribution and cloud-free sampling. We find that seasonal changes in aerosol vertical distribution, due to the seasonally varying mixing height, is the primary cause for the AOD and PM2.5 seasonal discrepancy, in particular, the low AOD but high PM2.5 observed during the winter season for Central and Western US. In addition, cloud-free sampling by passive sensors also induces some bias in AOD seasonality, especially for the Western US, where the largest seasonal change in cloud fraction is found. The seasonal agreement between low level (below 500 m AGL), all sky CALIOP AOD and PM2.5 is significantly better than column AOD from MODIS, MISR, SeaWiFS and OMI. In particular, the correlation between low level, all sky AOD and PM2.5 seasonal cycles increases to above 0.7 for Central and Western US, as opposed to near zero or negative correlation for column, clear sky AOD. This result highlights the importance of accounting for the seasonally varying aerosol profiles and cloud-free sampling bias when using column AOD measurements to infer surface PM2.5 concentrations.

MODIS (radiometry)↗
DOE OSTI2023

FPGA Acceleration of GCN in Light of the Symmetry of Graph Adjacency Matrix

Graph Convolutional Neural Networks (GCNs) are widely used to process large-scale graph data. Different from deep neural networks (DNNs), GCNs are sparse, irregular, and unstructured, posing unique challenges to hardware acceleration with regular processing elements (PEs). In particular, the adjacency matrix of a GCN is extremely sparse, leading to frequent but irregular memory access, low spatial/temporal data locality and poor data reuse. Furthermore, a realistic graph usually consists of unstructured data (e.g., unbalanced distributions), creating significantly different processing times and imbalanced workload for each node in GCN acceleration. To overcome these challenges, we propose an end-to-end hardware-software co-design to accelerate GCNs on resource-constrained FPGAs with the features including: (1) A custom dataflow that leverages symmetry along the diagonal of the adjacency matrix to accelerate feature aggregation for undirected graphs. We utilize either the upper or the lower triangular matrix of the adjacency matrix to perform aggregation in GCN to improve data reuse. (2) Unified compute cores for both aggregation and transform phases, with full support to the symmetry-based dataflow. These cores can be dynamically reconfigured to the systolic mode for transformation or as individual accumulators for aggregation in GCN processing. (3) Preprocessing of the graph in software to rearrange the edges and features to match the custom dataflow. This step improves the regularity in memory access and data reuse in the aggregation phase. Moreover, we quantize the GCN precision from FP32 to INT8 to reduce the memory footprint without losing the inference accuracy. We implement our accelerator design in Intel Stratix10 MX FPGA board with HBM2, and demonstrate 1.3x-110.5x improvement in end-to-end GCN latency as compared to the state-of the-art FPGA implementations, on the graph datasets of Cora, Pubmed, Citeseer and Reddit.

Nair, Gopikrishnan R.↗
NASA NTRSDate not supplied

Climatology of Global Precipitation Measurement Mission Precipitation Regimes and Implications for Global Estimates of Vertical Winds

The Global Precipitation Measurement (GPM) mission Validation Network (VN) framework leverages over 118 ground-based polarimetric Doppler radars to validate a large subset of precipitation measurements and retrievals from the GPM Dual-frequency Precipitation Radar (DPR). Recently, GPM DPR reflectivity profiles within the VN have been classified according to their convective regime using unsupervised machine learning techniques. The archetypal regimes are stratiform, convective, mixed stratiform-convective (e.g., transition regions), and “other” (e.g., peripheral regions of light precipitation). Subcategories within these four primary regimes vary according to the characteristic depth of included reflectivity profiles, resulting in 12 main GPM DPR precipitation profile categories. Polarimetry of ground-based Doppler radars in the VN offers additional insights into the types of precipitation, while pairs of radars positioned near each other enable retrieval of vertical winds via dual-Doppler analysis. Geometrically matched to the DPR reflectivity profiles in the GPM VN, these ground-based data and retrievals contribute more detailed characterization of the distinct kinematic and microphysical structures associated with each of the 12 DPR precipitation regimes. DPR reflectivity profiles linked with wind in the VN are restricted to GPM overpasses of proximal radar pairs that allow dual-Doppler analysis. Although a limited subset of DPR profiles in the VN are matched with vertical motion, agreement between the reflectivity structures paired with wind data and those of the greater DPR dataset in the VN suggest that estimates of vertical motion may be inferred in regions without ground-based measurements. We present a climatology of the 12 convective regimes identified within the DPR VN dataset as well as early efforts to estimate the kinematic and microphysical structures of precipitation profiles within the greater GPM DPR dataset by applying machine learning techniques. Precipitation data paired with global estimates of vertical winds from these efforts offer early insight to and support upcoming missions to retrieve convective mass flux, including the Investigation of Convective Updrafts (INCUS) in the Tropics and the global Atmosphere Observing System (AOS).

Precipitation↗
DOE OSTI2015

PROSPECTS FOR CHARACTERIZING HOST STARS OF THE PLANETARY SYSTEM DETECTIONS PREDICTED FOR THE KOREAN MICROLENSING TELESCOPE NETWORK

I investigate the possibility of constraining the flux of the lens (i.e., host star) for the types of planetary systems the Korean Microlensing Telescope Network is predicted to find. I examine the potential to obtain lens flux measurements by (1) imaging the lens once it is spatially resolved from the source, (2) measuring the elongation of the point-spread function of the microlensing target (lens+source) when the lens and source are still unresolved, and (3) taking prompt follow-up photometry. In each case I simulate the observing programs for a representative example of current ground-based adaptive optics (AO) facilities (specifically NACO on the Very Large Telescope), future ground-based AO facilities (GMTIFS on the Giant Magellan Telescope, GMT), and future space telescopes (NIRCAM on the James Webb Space Telescope, JWST). Given the predicted distribution of relative lens-source proper motions, I find that the lens flux could be measured to a precision of σ{sub H{sub ℓ}}≤0.1 for ≳60% of planet detections ≥5 yr after each microlensing event for a simulated observing program using GMT, which images resolved lenses. NIRCAM on JWST would be able to carry out equivalently high-precision measurements for ∼28% of events Δt = 10 yr after each event by imaging resolved lenses. I also explore the effects various blend components would have on the mass derived from prompt follow-up photometry, including companions to the lens, companions to the source, and unassociated interloping stars. I find that undetected blend stars would cause catastrophic failures (i.e., >50% fractional uncertainty in the inferred lens mass) for ≲ (16 · f {sub bin})% of planet detections, where f {sub bin} is the binary fraction, with the majority of these failures occurring for host stars with mass ≲0.3 M {sub ☉}.

79 ASTRONOMY AND ASTROPHYSICS↗