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Tensor Basis Neural Network for Scalar Mixing

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## 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

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