首页|Findings in the Area of Machine Learning Reported from Pennsylvania State University (Penn State) (Improving River Routing Using a Differentiable Muskingum-cunge Model and Physics-informed Machine Learning)
Findings in the Area of Machine Learning Reported from Pennsylvania State University (Penn State) (Improving River Routing Using a Differentiable Muskingum-cunge Model and Physics-informed Machine Learning)
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NETL
NSTL
Amer Geophysical Union
Researchers detail new data in Machine Learning. According to news originating from University Park, Pennsylvania, by NewsRx correspondents, research stated, "Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models-particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables. However, hydrologic routing simulations, necessary for simulating floods in stem rivers downstream of large heterogeneous basins, had not yet benefited from these advances and it was unclear if the routing process could be improved via coupled NNs." Funders for this research include United States Department of Energy (DOE), Cooperative Institute for Research, United States Department of Energy (DOE), U.S. Department of Interior, National Science Foundation (NSF).
University ParkPennsylvaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPennsylvania State University (Penn State)
NETL
NSTL
Amer Geophysical Union
