Accelerating Phase-field Predictions Via Surrogate Models Trained By Machine Learning Methods .
Abstract not provided.
Hu, Chongze↗
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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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Building a new generation of multiscale materials models with machine-learned interatomic potentials.
Abstract not provided.
McCarthy, Megan↗
Uncertainty Quantification of Machine Learning Interatomic Potential Models.
Abstract not provided.
Sargsyan, Khachik↗
Using Machine Learning for Predictive Modeling of Weather Impacts on Utility-scale Photovoltaic Systems.
Abstract not provided.
Mendoza, Hector↗
Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository.
Abstract not provided.
Debusschere, Bert↗
RaISE: A Framework to Characterize Surrogate Models in Scientific Machine Learning
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Morrow, Zachary Benjamin↗
Applications of machine learning in the modeling and discovery of metal-(super)hydrides
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Witman, Matthew David↗
P219: Developing high-fidelity, machine-learning-based spectral models
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Schaeuble, Marc-Andre↗
Developing Machine-Learning and Statistical Models for Western US Seasonal Precipitation Anomalies
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Strasberg, Zachary [University of New Mexico]↗
Scientific Machine Learning in Enhancing Model Predictability Through Model Form Error Quantification.
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Ohene-Obeng, Kwesi Appau [Sandia National Laborato↗
PreMevE: A Machine-learning Based Predictive Model for MeV Electrons inside Earth’s Outer Radiation Belt
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Heliospheric and Magnetospheric Physics↗
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
Dingreville, Remi Philippe Michel↗
Accelerating phase-field based predictions via surrogate models trained by machine learning methods.
Abstract not provided.
Dingreville, Remi Philippe Michel↗
Accelerating phase-field based predictions: A case study with surrogate models trained using machine learning methods.
Abstract not provided.
Dingreville, Remi Philippe Michel↗
Integrating Big Data for Applications in Machine Learning and Spatial Modeling to Forecast the Remaining Lifespan of Offshore Oil and Gas Infrastructure
Poster presented at AGU 2021 Fall Meeting, Virtual, December 13-17, 2021.
Romeo, Lucy↗
Predicting Self-Diffusion in Real and Model Systems using Machine Learning.
Abstract not provided.
Allers, Joshua Paul↗
Ground and Excited State Properties on the Intersection of Machine Learning and Semiempirical Models (and Chemical Bonding)
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Inorganic and Physical Chemistry↗
Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository.
Abstract not provided.
Debusschere, Bert↗