首页|Investigators from Pacific Northwest National Laboratory Release New Data on Machine Learning (Feature-adjacent Multi-fidelity Physics-informed Machine Learning for Partial Differential Equations)
Investigators from Pacific Northwest National Laboratory Release New Data on Machine Learning (Feature-adjacent Multi-fidelity Physics-informed Machine Learning for Partial Differential Equations)
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NETL
NSTL
Elsevier
A new study on Machine Learning is now available. According to news reporting originating from Richland, Washington, by NewsRx correspondents, research stated, “Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can still require high-fidelity data which can be expensive to generate.” Funders for this research include United States Department of Energy (DOE), United States Department of Energy (DOE), United States Department of Energy (DOE). Our news editors obtained a quote from the research from Pacific Northwest National Laboratory, “To reduce or even eliminate the dependency on high-fidelity data, we propose a novel multi-fidelity architecture which is based on a feature space shared by the low-and high-fidelity solutions. In the feature space, the representations of the low-fidelity and high-fidelity solutions are adjacent by constraining their relative distance. The feature space is represented with an encoder and its mapping to the original solution space is effected through a decoder.”
RichlandWashingtonUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPacific Northwest National Laboratory
NETL
NSTL
Elsevier
