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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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Establishing a data-driven deformation model for Tin using symbolic regression and genetic programming .
Abstract not provided.
The Early Career Research Program Annual Progress Report: Using ARM Data and Multiscale Models to Advance the Understanding of Liquid-Phase Cloud Response to Aerosol Perturbation over Ocean and Land
This report is for the first fiscal year of this ECRP project. This ECRP project aims to study the response of liquid-phase clouds to aerosol perturbation and reduce the related uncertainty in the DOE Earth system model. During this fiscal year, this project mainly made progress on the tasks about observational analyses for Objective 1; and successfully set up two Large-Eddy Simulation (LES) models for Objective 2. A postdoctoral researcher and a graduate student summer intern are on board and fully dedicated to this project. In addition to internal collaboration within LLNL, this project has routinely maintained external collaboration with scientists from universities, NASA Langley Research Center, and other DOE national laboratories. For the next reporting period, we plan to complete the tasks scheduled for the second year. Furthermore, we will promote the findings to communities of interest and proactively seek new collaborations to increase the impact of this project.
Data-Driven Compact Modeling of Bipolar Junction Transistors with Recurrent Neural Networks.
Abstract not provided.
Platinum equation of state to greater than 2 terapascals: experimental data and analytical models.
Abstract not provided.
The Early Career Research Program Annual Progress Report: Using ARM Data and Multiscale Models to Advance the Understanding of Liquid-Phase Cloud Response to Aerosol Perturbation over Ocean and Land
This project aims to study the response of liquid-phase clouds to aerosol perturbation and reduce the related uncertainty in the DOE Earth system model. Specific objectives include: Objective 1: Quantify the aerosol indirect effect on liquid-phase clouds using ARM observations and satellite retrievals while constraining the confounding large-scale conditions; Objective 2: Harness LES case studies, untangle the confounding meteorological impacts on the observed aerosol indirect effect, and identify key cloud processes controlling the net radiative forcing of aerosol-cloud interactions at different spatiotemporal scales; Objective 3: Evaluate the aerosol indirect effect on liquid-phase clouds with process-level understanding in E3SM multiscale model configurations.
Platinum equation of state > 2 TPa: experimental data and analytical models.
Abstract not provided.
Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations.
Abstract not provided.
Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations.
Abstract not provided.
Automated analysis of unlabeled PV data with Solar Data Tools software: Overview and feature updates
Distributed rooftop PV systems: ubiquitous, yet commonly have unlabeled data Difficult or impossible to form a performance index We developed Solar Data Tools (SDT), an open-source Python library for analyzing PV power (and irradiance) time-series data SDT enables analysis of unlabeled PV data—no model, no meteorological data, no performance index required Takes a statistical signal processing approach Data processing steps are largely pre-defined and automatic regardless of system type—from utility tracking systems to multi-pitch rooftop systems
Data-Driven Light Model for the MicroBooNE Experiment
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Establishing a data-driven strength model for 𝜷-tin by performing symbolic regression using genetic programming
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Sandia Analysis Workbench (SAW) Workflow using Xyce-PyMi for Simulating Circuit-Level Response using Data-Driven Device Models
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Transfer Learning of Reactor Power Models with Nonradiological Data
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