SEARCH · Engineering Papers
Results for “Surrogates”
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.
International Multi-Modal Surrogate Spent Nuclear Fuel Transportation Test: The SNF Transportation Test Triathlon.
Abstract not provided.
Airborne Release Fraction (ARF) of Nuclear Waste Surrogates.
Abstract not provided.
Black-Box Optimization with Local Generative Surrogates
Explore the source record for details and available documents.
Vertical Flume Testing of WIPP Surrogate Waste Materials.
Abstract not provided.
Neural-Inspired Computation for Efficient Scientific Computing via Random Walks and Surrogate Models.
Abstract not provided.
Surrogate Modeling of Spatially Heterogeneous Source Terms for Probabilistic Assessment of Repository Performance.
Abstract not provided.
Evaluation of Time-step Frequency on Prediction Accuracy Applied to Deep Learning Neural Network Surrogate Models for CO2 Storage
This PowerPoint presentation was presented virtually at the American Geophysical Union (AGU) Fall Meeting 2020.
Efficient Training of Neural Network Surrogate Methods with Variable Projection.
Abstract not provided.
Airborne Release Fractions from Fires Containing Solid Surrogate Nuclear Waste.
Abstract not provided.
Kinetic modeling of NO and NO2 promotion/inhibition interactions with gasoline surrogate components
Explore the source record for details and available documents.
Quantum Annealing Approaches for Building Sparse Surrogate Models in Uncertainty Quantification.
Abstract not provided.
Quantifying the effect of multiple demand response actions on electricity demand and building services via surrogate modeling
Explore the source record for details and available documents.
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.