Experiences developing large-scale synthetic U.S.-style distribution test systems
Not Available
Palmintier, Bryan (ORCID:0000000214520715)↗
SEARCH · Search NASA
Results for “synthetic data sets”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Development of Machine Learning Algorithm for Pebble Bed Modular Reactor Misuse Detection
The objective of this work was to develop a machine learning ensemble that could assist pebble bed reactor verification by evaluating whether a given pebble circulating through a PBR was normal or anomalous using gamma spectroscopy measurements from a notional PBR burnup measurement system. Using a PBR reference design, data sets of synthetic gamma spectra representative of BUMS measurements of normal and anomalous pebbles that may be used to produce special fissile material were generated to train and test an ML anomaly detection ensemble on two reference scenarios – substitution of normal pebbles with target pebbles for production of Pu or 233 U. The ML ensemble correctly identified all anomalous pebbles in the testing data set, and while perfect ensemble performance is normally indicative of overfitting, it was concluded that significantly lower photon intensity of target pebbles produced distinctly less intense photon spectra to where perfect ensemble performance was expected.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Pycheron: A Python-Based Seismic Waveform Data Quality Control Software Package
Supplementing an existing high-quality seismic monitoring network with openly available station data could improve coverage and decrease magnitudes of completeness; however, this can present challenges when varying levels of data quality exist. Without discerning the quality of openly available data, using it poses significant data management, analysis, and interpretation issues. Incorporating additional stations without properly identifying and mitigating data quality problems can degrade overall monitoring capability. If openly available stations are to be used routinely, a robust, automated data quality assessment for a wide range of quality control (QC) issues is essential. To meet this need, we developed Pycheron, a Python-based library for QC of seismic waveform data. Pycheron was initially based on the Incorporated Research Institutions for Seismology’s Modular Utility for STAtistical kNowledge Gathering but has been expanded to include more functionality. Pycheron can be implemented at the beginning of a data processing pipeline or can process stand-alone data sets. Its objectives are to (1) identify specific QC issues; (2) automatically assess data quality and instrumentation health; (3) serve as a basic service that all data processing builds on by alerting downstream processing algorithms to any quality degradation; and (4) improve our ability to process orders of magnitudes more data through performance optimizations. This article provides an overview of Pycheron, its features, basic workflow, and an example application using a synthetic QC data set.
58 GEOSCIENCES↗
Coincident learning for unsupervised anomaly detection of scientific instruments
Abstract Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to components. While scientific facilities generate a wealth of data, labeled anomalies may be rare (or even nonexistent), and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called coincident learning for anomaly detection (CoAD), which is specifically designed for multi-modal tasks and identifies anomalies based on coincident behavior across two different slices of the feature space. We define an unsupervised metric, F ^ β , out of analogy to the supervised classification F β statistic. CoAD uses F ^ β to train an anomaly detection algorithm on unlabeled data , based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and our motivating task of identifying RF station anomalies in a particle accelerator.
43 PARTICLE ACCELERATORS↗
Efficient 3-D velocity model building using joint inline and crossline plane-wave wave-equation migration velocity analyses
SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.
Liu, Xuejian↗
Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification
3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.
Geology↗
The Catalogue for Astrophysical Turbulence Simulations (CATS)
Turbulence is a key process in many fields of astrophysics. Advances in numerical simulations of fluids over the last several decades have revolutionized our understanding of turbulence and related processes such as star formation and cosmic ray propagation. However, data from numerical simulations of astrophysical turbulence are often not made public. We introduce a new simulation-oriented database for the astronomical community: the Catalogue for Astrophysical Turbulence Simulations (CATS), located at www.mhdturbulence.com. CATS includes magnetohydrodynamic (MHD) turbulent box simulation data products generated by the public codes athena++, arepo, enzo, and flash. CATS also includes several synthetic observational data sets, such as turbulent HI data cubes. We also include measured power spectra and three-point correlation functions from some of these data. We discuss the importance of open-source statistical and visualization tools for the analysis of turbulence simulations such as those found in CATS.
79 ASTRONOMY AND ASTROPHYSICS↗
Estimating the Single-Trial Characteristics of Event-Related Responses: Evaluation of the MCERP Algorithm
Single-trial event-related responses collected during the course of an experiment are typically averaged before analysis resulting in a rather crude picture of event-related brain dynamics. It has been quite clear for some time that these responses exhibit trial-to-trial variability: however, the computational techniques necessary to deal with such responses in noisy conditions have not been available. To this end we have developed the multiple-component, event-related potential model (mcERP), which assumes that the each event-related response consists of a sum of multiple evoked components each described by a stereotypical waveshape. These waveshapes are allowed to vary in amplitude and onset latency from trial to trial, which allows us to capture, to first-order, the trial-dependent variations in event-related brain dynamics. We have constructed many sets of synthetic data designed to simulate intracortical recordings from a 15 channel, linear-array multielectrode implanted acutely in V1 of an awake-behaving macaque undergoing visual stimulation with a red light flash. This synthetic data was used to characterize the performance of the mcERP algorithm. First we quantified the degree to which such trial-to-trial variability aids in the identification of multiple components, and we demonstrate that amplitude variability is a more important factor in component separation than latency variability. Second, we quantified the behavior of the algorithm under two distinct signal-to-noise ratio (SNR) conditions: Gaussian noise independently present in each channel, and highly correlated (1/f distributed), far-field noise presented identically in each channel of the array. The mcERP algorithm was found to be robust to noise accurately identifying all component waveshapes and their associated single-trial characteristics down to SNR levels of -20dB for Gaussian noise and -7dB for 1/f far-field noise. Comparisons of the performance of this algorithm with factor analysis (FA) and independent component analysis (ICA) will be described by Knuth et al. (SFN abstracts, 2002). In addition, the advantages of application of mcERP to real data will be described by Shah et al, (these abstracts, 2002: SFN abstracts, 2002).
Knuth, K. H.↗
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
Machine learning (ML) is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using ML algorithms for atmospheric retrieval by implementing a random forest (RF) to perform retrievals in seconds that are consistent with the traditional, computationally expensive nested-sampling retrieval method. We expand upon their approach by presenting a new ML model, plan-net, based on an ensemble of Bayesian neural networks (BNNs) that yields more accurate inferences than the RF for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use BNNs for atmospheric retrieval, we also introduce a new loss function for BNNs that learns correlations between the model outputs. Importantly, we show that designing ML models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply plan-net to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher resolution spectra and a larger number of atmospheric parameters.
Adam D. Cobb↗
Developing Synthetic Distribution Models Using Open-Source Data Sets
The deployment of distributed generation resources at scale with the distribution side of the power system network over the last two decades has spurred a lot of research interest in distribution networks. Given, the sensitive nature of power system data sets, utilities are still reluctant to open-source distribution networks. Although some open-source data sets are available, they may not cover the region of interest. In this paper, we introduce the Synthetic dIstribution Network Generator (SING), new software that allows users to develop synthetic distribution models, using open-source data sets, in any region of interest within the continental US.
data set↗
Assessment of Local Observation of Atomic Ordering in Alloys via the Radial Distribution Function: A Computational and Experimental Approach
As a powerful analytical technique, atom probe tomography (APT) has the capacity to acquire the spatial distribution of millions of atoms from a complex sample. However, extracting information at the Ångstrom-scale on atomic ordering remains a challenge due to the limits of the APT experiment and data analysis algorithms. The development of new computational tools enable visualization of the data and aid understanding of the physical phenomena such as disorder of complex crystalline structures. Here, we report progress towards this goal using two steps. We describe a computational approach to evaluate atomic ordering in the crystal structure by generating radial distribution functions (RDF). Atomic ordering is rendered as the Fractional Cumulative Radial Distribution Function (FCRDF) which allows for greater visibility of local compositions at short range in the structure. Further, we accommodate in the analysis additional parameters such as uncertainty in the atomic coordinates and the atomic abundance to ascertain short-range ordering in APT data sets. We applied the FCRDF analysis to synthetic and experimental APT data sets for Ni 3 Al. The ability to observe a signal of atomic ordering consistent with the known L1 2 crystal structure is heavily dependent on spatial uncertainty, irrespective of abundance. Detection of atomic ordering is subject to an upper limit of spatial uncertainty of atoms described with Gaussian distributions with a standard deviation of 1.3 Å. The FCRDF analysis was also applied to the APT data set for a six-component alloy, Al 1.3 CoCrCuFeNi. In this case, we are currently able to visualize elemental segregation at the nanoscale, though unambiguous identification of atomic ordering at the Ångstrom (nearest-neighbor) scale remains a goal.
36 MATERIALS SCIENCE↗
An atlas of November 1978 synthetic aperture radar digitized imagery for oil spill studies
A data set is described which consists of digitized synthetic aperture radar (SAR) imagery plus correlative data and some preliminary analysis results. This data set should be of value to experimenters who are interested in the SAR instrument and its application to the detection and monitoring of oil on water and other distributed targets.
Maurer, H. E.↗
Solving differential equations using deep neural networks
Recent work on solving partial differential equations (PDEs) with deep neural networks (DNNs) is presented. The paper reviews and extends some of these methods while carefully analyzing a fundamental feature in numerical PDEs and nonlinear analysis: irregular solutions. First, the Sod shock tube solution to the compressible Euler equations is discussed and analyzed. This analysis includes a comparison of a DNN-based approach with conventional finite element and finite volume methods, and demonstrates that the DNN is competitive in terms of degrees of freedom required for a given accuracy. Further, the DNN-based approach is extended to consider performance improvements and simultaneous parameter space exploration. Next, a shock solution to compressible magnetohydrodynamics (MHD) is solved for, and used in a scenario where experimental data is utilized to enhance a PDE system that is a priori insufficient to validate against the observed/experimental data. This is accomplished by enriching the model PDE system with source terms that are then inferred via supervised training with synthetic experimental data. The resulting DNN framework for PDEs enables straightforward system prototyping and natural integration of large data sets (be they synthetic or experimental), all while simultaneously enabling single-pass exploration of an entire parameter space.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets
X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl 3 -calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. Here, the models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)
Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.
36 MATERIALS SCIENCE↗
A D-term Modeling Code (DMC) for Simultaneous Calibration and Full-Stokes Imaging of Very Long Baseline Interferometric Data
In this paper we present DMC, a model and associated tool for polarimetric imaging of very long baseline interferometry data sets that simultaneously reconstructs the full-Stokes emission structure along with the station-based gain and leakage calibration terms. DMC formulates the imaging problem in terms of posterior exploration, which is achieved using Hamiltonian Monte Carlo sampling. The resulting posterior distribution provides a natural quantification of uncertainty in both the image structure and the data calibration. We run DMC on both synthetic and real data sets, the results of which demonstrate its ability to accurately recover both the image structure and calibration quantities, as well as to assess their corresponding uncertainties. The framework underpinning DMC is flexible, and its specific implementation is under continued development.
47 OTHER INSTRUMENTATION↗
Methods for local gravity field approximation
The most widely known modern method for estimating gravity field values from observed data is least-squares collocation. Its advantages are that it can make estimates at arbitrary locations based on irregularly spaced observations, and that it makes use of statistical information about errors in the input data while providing corresponding information about the quality of the output estimates. Disadvantages of collocation include the necessity of inverting square matrices of dimension equal to the number of data values and the need to assume covariance models for the gravity field and the data errors. Fourier methods are an important alternative to collocation; having the advantage of greater computational efficiency, but requiring data estimates to be on a regular grid and not using or providing statistical accuracy information. The GEOFAST algorithm is an implementation of collocation that achieves high computational efficiency by transforming the estimation equations into the frequency domain where an accurate approximation may be made to reduce the workload. The forward and inverse Fast Fourier Transforms (FFTs) are utilized. The accuracy and computational efficiency of the GEOFAST algorithm is demonstrated using two sets of synthetic gravity data: marine gravity for an ocean trench region including wavelengths longer than 200 km; and local land gravity containing wavelengths as short as 5 km. These results are discussed along with issues such as the advantages of first removing reference field models before carrying out the estimation algorithm.
Sailor, R. V.↗
Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System
Abstract Enhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.
15 GEOTHERMAL ENERGY↗