Search Results

Search Constraints

Start Over
Filtering by: Depositor ssim jakecar@umich.edu Remove constraint Depositor ssim: jakecar@umich.edu Discipline Science Remove constraint Discipline: Science

Search Results

  • Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages

    User Collection
    collection
    Creator:
    Limon, Garrett
    Description:
    The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
    Keyword:
    Machine Learning, Climate Modeling, and Physics Emulators
    Discipline:
    Science and Engineering
    2Works

  • Canadian Cordillera Fault Gouge XRD and isotopes

    work
    Creator:
    Lynch, Erin and van der Pluijm, Ben
    Description:
    High-resolution, low-angle XRD analysis of oriented clay samples (.txt files) and TC/EA, Mass Spectronometric analysis of oxygen and hydrogen isotopes (.xslx files)
    Keyword:
    fault gouge and geofluids
    Citation to related publication:
    In submission
    Discipline:
    Science