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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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Intra-class data augmentation with deep generative models of threat objects in baggage radiographs
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Big Microstructure Datasets for Materials Informatics: Using Statistically Conditioned Generative Models to Curate Big Datasets
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Designing Large Datasets: Data-Scarce and Stable Deep Generative Models for Turning Sparse Experiments into Big Datasets in Materials Science
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Big Microstructure Datasets for Materials Informatics: Using Statistically Conditioned Generative Models to Curate Big Datasets
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Physics-informed Deep Generative Models to Quantify Uncertainties in Geophysical Full-waveform Inversion
SSA oral presentation
Local-Global Decompositions: Data-scarce and Stable Deep Generative Models for Turning Sparse Experiments into Big Datasets in Materials Science
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Learning Shock Hydrodynamics with Generative Models
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Next-Generation Modeling Approaches for Exhaust Control and Divertor Optimization
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Learning turbulence with machines: data-driven closures and generative models
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Multivariant function model generation
The development of computer programs applicable to space vehicle guidance was conducted. The subjects discussed are as follows: (1) determination of optimum reentry trajectories, (2) development of equations for performance of trajectory computation, (3) vehicle control for fuel optimization, (4) development of equations for performance trajectory computations, (5) applications and solution of Hamilton-Jacobi equation, and (6) stresses in dome shaped shells with discontinuities at the apex.
Supporting Research and Technology (SRT) session: Status of yield estimation technology, a review of second-generation model development
There are no author-identified significant results in this report.
Status of yield estimation technology: A review of second-generation model development and evaluation
Multiple regression models were studied in order to determine their yield estimation capability for any arbitrary unit area and to obtain greater responsiveness and accuracy through the use of additional data sources applied at smaller spatial and temporal scales. It was concluded that data base inadequacy was the factor limiting performance in the models studied and that each of the models has more yield predicting capability than was reached during LACIE.
The thermal influence of continents on a model-generated January climate
Two climate simulations were compared. Both climate computations were initialized with the same horizontally uniform state of rest. However, one is carried out on a water planet (without continents), while the second is repeated on a planet with geographically realistic but flat (sea level) continents. The continents in this experiment have a uniform albedo of 0.14, except where snow accumulates, a uniform roughness height of 0.3 m, and zero water storage capacity. Both runs were carried out for a 'perpetual January' with solar declination fixed at January 15.
Comparison of GLAS retrieved cloud fields with model generated rainfall fields
Monthly mean fractional cloud cover for January and February 1979, retrieved from SOP 1 of FGGE, are compared with the total precipitation field derived diagonally from the GLAS analysis/forecast system for the same time period. The breakdown of cloudiness into day (3 AM) and night (3 PM) is consistent with maps of outgoing long wave and short wave radiation inferred from AVHRR data. Of the many regions of coincidence, there is a particularly striking phenomenon: west of the coast of Peru, at about 20 deg S, there is a distinct small scale maximum in precipitation which coincides precisely with a maximum in the cloudiness field. This maximum in cloudiness and precipitation does not appear in the NOAA/NESS fields of albedo and outgoing long wave radiation which are normally sensitive to cloud fields. These low level clouds with warm tops are a mainly nocturnal phenomenon.
Generation models of electron conics
Electron distribution functions (EDFs) with a peak oblique to the magnetic field, adjacent to but distinct from loss-cone features, have been observed by the DE 1, Viking, and S3-3 spacecraft in passes through the nightside auroral zone, polar cap, dayside cusp, and extended dayside auroral oval. Using particle simulations, two types of wave excitation and particle acceleration mechanisms which may contribute to producing these electron conic distributions are investigated. The first involves excitation of upper-hybrid waves by the electron loss cone, and the subsequent perpendicular heating of the background and thermal electrons. The second involves excitation of downward propagating parallel modes by an auroral electron beam which frequently accompanies the upflowing electron conics. These modes provide parallel acceleration, which modifies the GDF. Those electrons which are not lost to the atmosphere and mirror back up the magnetic field line give rise to enhancements in the GDF at the edge of the loss cone.
Representing the Unknown in Computer Simulations: From Reduced-Order to Generative Models
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Data-driven model generation for deception defence of cyber-physical environments
Cyber-physical systems have the potential to greatly improve both the economic and the environmental efficiency of our current infrastructure. However, the necessary fusing of the cyber world with the physical world that results from these technologies also creates a host of new attack opportunities. In this work, we discuss how deception can be employed to effectively defend these systems.