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Source code and examples for Evolutional Deep Neural Network (EDNN)
License: MIT License
This project forked from ydume/ednn
Source code and examples for Evolutional Deep Neural Network (EDNN)
License: MIT License
# Yifan Du [email protected] # Tamer A. Zaki [email protected] # Johns Hokpins University # US patent submitted 03/08/2021 This main folder containts several realization of EDNN for different time dependent partial differential equations. In each folder there are four files: main.py : The main function for this problem. It contains the problem definition and parameters, the high level initialization, training and marching commands for the EDNN. ednn.py : The EDNN abstract class. It contains the detailed implementation of EDNN structure, training and marching. Because the boundary conditions and constraints for different PDEs are different, the ednn files are slightly different for these cases. rhs.py : The evolutional PDE implementation. It containts The right -hand-side of different PDEs. These nonlinear differential operators are implemented using tensorflow. marching_schemes : The time marching methods for the neural network parameters. The forward Euler and Runge-Kutta are implemented. To run one of these cases, first enter the corresponding directory. To train the network for initial condition, type: python main.py 0 To march the trained network, type: python main.py 1
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