首页|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)
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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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.
BerkeleyCaliforniaUnited StatesNor th and Central AmericaAlgorithmsCyborgsEmerging TechnologiesMachine Lear ningOptimization AlgorithmsLawrence Berkeley National Laboratory
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