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Deep Reinforcement Learning for solving Partial Differential Equations

Usage

Setup your PDE, domain, boundary and initial conditions in .py file See parameters.py for the default setup

In a jupyter notebook, import the main file by using the line

import DRLPDE.main

and run the program by calling the name of the .py file (excluding the .py extension)

model = DRLPDE.main.solvePDE('my_pde')

Other arguments can be passed to modify the training parameters. The default parameters can be found in the define_solver_parameters function of DRLPDE.main.py

main.ipynb is a Jupyter notebook implementing this exactly.

Pre-built examples:

JCPexample1

Steady Stokes flow in a rotating disk model = DRLPDE.main.solvePDE('JCPexample1')

  • Has analytic solution

JCPexample2

Unsteady Stokes flow in a rotating disk model = DRLPDE.main.solvePDE('JCPexample2')

  • Has analytic solution

JCPexample3

Steady Stokes flow in rotating sphere model = DRLPDE.main.solvePDE('JCPexample3')

  • Has analytic solution

JCPexample4

Poiseuille Flow in a circular pipe model = DRLPDE.main.solvePDE('JCPexample4')

  • Has analytic solution

JCPexample5

Cavity Stokes Flow model = DRLPDE.main.solvePDE('JCPexample5')

  • No analytic solution

JCPexample6

Flow Past Disk model = DRLPDE.main.solvePDE('JCPexample6')

  • No analytic solution

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Deep Reinforcement Learning of Partial Differential Equations

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