Computational Modeling and Machine Learning Projects Supporting the Human Research Program at NASA
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Mona Matar↗
SEARCH · Engineering Papers
Results for “machine learning models”
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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Using machine learning to model and predict water clarity in the Great Lakes
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Cameron C. Lee↗
PV Monitoring and Modeling a Machine Learning Perspective.
Abstract not provided.
Jones, Christian Birk↗
Reduced-order modeling: using machine learning to enable large-scale simulation for many query problems.
Abstract not provided.
Carlberg, Kevin Thomas↗
Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulation for many-query problems.
Abstract not provided.
Carlberg, Kevin Thomas↗
Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulations for real time and many-query problems.
Abstract not provided.
Carlberg, Kevin Thomas↗
Machine-learning error models for quantifying the epistemic uncertainty in low-fidelity models.
Abstract not provided.
Carlberg, Kevin Thomas↗
Nonlinear Reduced-Order Modeling: Using Machine Learning to Enable Extreme-Scale Simulation for Many-Query Problems.
Abstract not provided.
Carlberg, Kevin Thomas↗
Machine Learning Closure Modeling for Reduced-Order Models of Dynamical Systems.
Abstract not provided.
Parish, Eric Joshua↗
Nonlinear reduced-order modeling: Using machine learning to enable extreme-scale simulations for many-query problems.
Abstract not provided.
Carlberg, Kevin Thomas↗
Time-series Machine Learning Error Models for Appproximate Solutions to Dynamical Systems.
Abstract not provided.
Parish, Eric Joshua↗
Nonlinear reduced-order modeling: using machine learning to enable extreme-scale simulation for many-query problems.
Abstract not provided.
Carlberg, Kevin Thomas↗
Use of physics to improve solar forecast: Part II, machine learning and model interpretability
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54 ENVIRONMENTAL SCIENCES↗
Machine learning predictive modeling of creep rupture time in high temperature alloys
10th International Conference on Multiscale Materials Modeling, Baltimore, MD, September 30-October 5, 2022
Wenzlick, Madison↗
Efficient Sampling Methods for Machine Learning Error Models with Application to Surrogates of Steady Hypersonic Flows.
Abstract not provided.
Krath, Elizabeth↗