Metadata-Version: 1.2
Name: tbnns
Version: 0.5.0
Summary: TBNN-s - Tensor Basis Neural Network for Scalar Mixing
Home-page: https://github.com/pmmilani/tbnns.git
Author: Pedro M. Milani
Author-email: pmmilani@stanford.edu
License: Apache
Description: ## TBNN-s v0.5.0 - Tensor Basis Neural Network for Scalar Mixing
        
        This package implements the vanilla Tensor Basis Neural Network [1]
        as the TBNN class, and also the Tensor Basis Neural Network for
        Scalar Flux Modeling [2] as the TBNNS class. They are described in
        the following references:
        
        [1] Ling, Kurzawski, Templeton. "Reynolds averaged turbulence modelling using deep neural networks with embedded invariance." J. Fluid Mech. 807 (2016)
        
        [2] Milani, Ling, Eaton. "Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling" J. Fluid Mech. (under review) 
        
        Author: Pedro M. Milani (email: pmmilani@stanford.edu)
        
        Last modified: 08/06/2020
        
        Developed and tested in Python 3.7 using tensorflow 1.15
        
        ### Installation
        To install, run the following (optionally within a virtual environment): 
        
            pip install tbnns [--user] [--upgrade]
            
        This will install the stable version from the Python Package Index. Use
        the flag --user in case you do not have administrator privileges and the
        flag --upgrade to get the newest version.
            
        To test the program while it is being developed, run the command below
        from the current directory. This is useful when you are developing the
        code.
        
            pip install -e .
            
        To uninstall, run:
            
            pip uninstall tbnns
            
        The commands above will also install
        some dependencies (included in the file "requirements.txt")
        needed for this package.
        
        ### Examples and Testing
        
        The folder test contains a script example_usage.py and three representative
        datasets. For an example of training a TBNN-s and applying it to a test
        set, run the following inside the folder test:
        
            python example_usage_tbnns.py
            
            python example_usage_tbnn.py
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
