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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.
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E3SM Atmosphere surrogate construction and calibration using machine learning and reduced order modeling .
Abstract not provided.
Wagman, Benjamin↗
Probabilistic Neural Network Surrogates for Reduced-Order Dynamics in Reacting Flows .
Abstract not provided.
Casey, Tiernan↗
Predicting plastic anisotropy using crystal plasticity and Bayesian neuralnetwork surrogate models.
Abstract not provided.
Lim, Hojun↗
Constructing Surrogates for Combustion Chemistry:Operator Learning for Reduced Order Dynamics.
Abstract not provided.
Venturi, Simone↗
A machine learning surrogate for density functional theory based on the local density of state.
Abstract not provided.
Modine, Normand↗
Automated machine learning for E3SM Atmosphere surrogate modeling.
Abstract not provided.
Chowdhary, Kamaljit↗
Facilitating Atmospheric Source Inversion via Deep Operator Network Surrogates.
Abstract not provided.
Gulian, Mamikon↗
Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ.
Abstract not provided.
Wildey, Timothy↗
Computational Challenges in the development of a surrogate model for Density Functional Theory calculations.
Abstract not provided.
Rajamanickam, Sivasankaran↗
RaISE: A Framework to Characterize Surrogate Models in Scientific Computing.
Abstract not provided.
Morrow, Zachary↗
Estimating Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models.
Abstract not provided.
Wildey, Timothy↗
Enhancing polynomial chaos expansion based surrogates through probabilistic transfer learning.
Abstract not provided.
Khalil, Mohammad↗
Machine-Learned Surrogate Models for Threaded Fastener Geometries Subjected to Multiaxial Loadings.
Abstract not provided.
Mersch, John↗
Leveraging Physics-based Surrogates for Efficient Density Estimation of Sparse Observable Data on Low-dimensional Manifolds.
Abstract not provided.
Yen, Tian Yu↗