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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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Frictional Properties of Two-Dimensional Materials: Data-Driven Machine Learning Predictive Modeling
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A Data-driven Approach to Harvesting Latent Reduced Models to Precondition Lossy Compression for Scientific Data
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Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report
Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.
Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws.
Abstract not provided.
Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real-Time Forecasting.
Abstract not provided.
Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real-Time Forecasting.
Abstract not provided.
Continuum semiconductor physics model compression via Data-driven Discrete Exterior Calculus.
Abstract not provided.
Improving accuracy of stochastic collocation in ODE/PDE systems via data-driven factorization of the model dynamics
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Improving accuracy of stochastic collocation in ODE/PDE systems via data-driven factorization of the model dynamics
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Data-driven Reduced-order Modeling for Optimization and Uncertainty Quantification
Short presentation for the Fellows Day Symposium.
Learning turbulence with machines: data-driven closures and generative models
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Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real Time Forecasting.
Abstract not provided.