How Generalizable is a Machine-Learning Approach for Modeling Hub-Height Turbulence Intensity? [electronic resource]
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2022
	Bộ sưu tập: Metadata
	ddc:  621.531  Thêm vào giỏ

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble [electronic resource]
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2021
	Bộ sưu tập: Metadata
	ddc:  621.45  Thêm vào giỏ

New methods to improve the vertical extrapolation of near-surface offshore wind speeds [electronic resource]
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2021
	Bộ sưu tập: Metadata
	ddc:  621.45  Thêm vào giỏ

How accurate is a machine learning-based wind speed extrapolation under a round-robin approach? [electronic resource]
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2020
	Bộ sưu tập: Metadata
	ddc:  621.531  Thêm vào giỏ

Enhancement of Unsteady and 3D Aerodynamics Models using Machine Learning [electronic resource]
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2020
	Bộ sưu tập: Metadata
	ddc:  621.45  Thêm vào giỏ

CAT [electronic resource] : computer aided triage improving upon the Bayes risk through ?-refusal triage rules
	Tác giả:
	Xuất bản: Oak Ridge Tenn Oak Ridge Tenn: Oak Ridge National Laboratory Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2018
	Bộ sưu tập: Metadata
	ddc:  570  Thêm vào giỏ

Machine learning to predict biomass sorghum yields under future climate scenarios [electronic resource]
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Science Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2020
	Bộ sưu tập: Metadata
	ddc:   Thêm vào giỏ

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning [electronic resource] : A Methodological Approach
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Science Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2020
	Bộ sưu tập: Metadata
	ddc:  623.2  Thêm vào giỏ

Learning curves for drug response prediction in cancer cell lines [electronic resource]
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Science Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2021
	Bộ sưu tập: Metadata
	ddc:  577.3  Thêm vào giỏ

Deep learning for Alzheimer's disease [electronic resource] : Mapping large-scale histological tau protein for neuroimaging biomarker validation
	Tác giả:
	Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Science Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2021
	Bộ sưu tập: Metadata
	ddc:  616.831  Thêm vào giỏ