Robotics & Machine Learning Daily News2024,Issue(Oct.8) :68-69.

Studies from Lawrence Berkeley National Laboratory Describe New Findings in Mach ine Learning (Efficient Inverse Design Optimization Through Multi-fidelity Simul ations, Machine Learning, and Boundary Refinement Strategies)

Robotics & Machine Learning Daily News2024,Issue(Oct.8) :68-69.

Studies from Lawrence Berkeley National Laboratory Describe New Findings in Mach ine Learning (Efficient Inverse Design Optimization Through Multi-fidelity Simul ations, Machine Learning, and Boundary Refinement Strategies)

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Abstract

2024 OCT 08 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news reporting out of Berkeley, California, by NewsRx editors, research stated, "This paper introduces a methodology designed to augment the i nverse design optimization process in scenarios constrained by limited compute, through the strategic synergy of multi-fidelity evaluations, machine learning mo dels, and optimization algorithms. The proposed methodology is analyzed on two d istinct engineering inverse design problems: airfoil inverse design and the scal ar field reconstruction problem." Financial supporters for this research include United States Department of Energ y (DOE), United States Department of Energy (DOE), U.S. Department of Energy Off ice of Science, Office of Advanced Scientific Computing Research, Scientific Dis covery through Advanced Computing (SciDAC) program through the FASTMath Institut e, Laboratory Directed Research and Development Program of the National Renewabl e Energy Laboratory.

Key words

Berkeley/California/United States/Nor th and Central America/Algorithms/Cyborgs/Emerging Technologies/Machine Lear ning/Optimization Algorithms/Lawrence Berkeley National Laboratory

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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