首页|New Findings from Sandia National Laboratories in the Area of Machine Learning Described (Accelerating Fem-based Corrosion Predictions Using Machine Learning)
New Findings from Sandia National Laboratories in the Area of Machine Learning Described (Accelerating Fem-based Corrosion Predictions Using Machine Learning)
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NETL
NSTL
Electrochemical Soc Inc
Researchers detail new data in Machine Learning. According to news reporting from Albuquerque, New Mexico, by NewsRx journalists, research stated, “Atmospheric corrosion of metallic parts is a widespread materials degradation phenomena that is challenging to predict given its dependence on many factors (e.g. environmental, physiochemical, and part geometry). For materials with long expected service lives, accurately predicting the degree to which corrosion will degrade part performance is especially difficult due to the stochastic nature of corrosion damage spread across years or decades of service.” Funders for this research include United States Department of Energy (DOE), United States Department of Energy (DOE).
AlbuquerqueNew MexicoUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSandia National Laboratories
NETL
NSTL
Electrochemical Soc Inc
