In this repository, there are three main working directories:
- ReactionRateCoefficientsRegression
- TransportCoefficientsRegression
- PINN_Euler_1d_shock_STS
- python 3 (with python 2 there could be some problems)
- scikit-learn: https://scikit-learn.org/stable/install.html
- Tensorflow 1.4 or 1.5 (superior may not work properly)
- Keras
In this directory, we do the regression of reaction rate coefficients according to the state-to-state (STS) theory.
In Utilities there are few functions for plotting (not interesting for you).
In docs there are some documents which may be useful for you to read.
From Project_Machine_Learning_Algorithms/ReactionRateCoefficientsRegression/DR/src run:
./run_regression.sh
This will learn one vibrational level at the time.
Otherwise,
./run_regression_multioutput.sh
This will learn all the dataset at once.
In both cases, the model, scalers and figures will be saved in model, scaler and pdf folders, respectively.
Only the scripts for dissociation are present. You can write the scripts for recombination (DR). In the same way, I only considered DR processes (datasets) here, neglecting VT, VV, VV2, and ZR. You can also try them.
In this directory, we do the regression of transport coefficients according to the state-to-state (STS) theory. In particular, we would like to predict:
- shear viscosity
- bulk viscosity
- thermal conductivity
- thermal diffusion
- mass diffusion
For a rigorous definition of the transport coefficients, please refer to the book: Nagnibeda, E., & Kustova, E. (2009). Non-equilibrium reacting gas flows: kinetic theory of transport and relaxation processes. Springer Science & Business Media.
In this directory, we do want to try to use PINNs to solve the Euler equations for a one-dimensional shock flow problem, according to the STS formuation.
There may be useful to take a look at some other repositories to better understand PINNs, for example: