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Fixing tutorials grammar (#242)

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* grammar check and sparse rephrasing
* rst created
* meta copyright adjusted
This commit is contained in:
Giuseppe Alessio D'Inverno
2024-03-05 10:43:34 +01:00
committed by GitHub
parent 15136e13f8
commit b10e02103b
23 changed files with 272 additions and 237 deletions
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8
tutorials/tutorial3/tutorial.py vendored
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@@ -34,7 +34,7 @@ from pina import Condition, Plotter
# \end{cases}
# \end{equation}
#
# where $D$ is a square domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one.
# where $D$ is a squared domain $[0,1]^2$, and $\Gamma_i$, with $i=1,...,4$, are the boundaries of the square, and the velocity in the standard wave equation is fixed to one.
# Now, the wave problem is written in PINA code as a class, inheriting from `SpatialProblem` and `TimeDependentProblem` since we deal with spatial, and time dependent variables. The equations are written as `conditions` that should be satisfied in the corresponding domains. `truth_solution` is the exact solution which will be compared with the predicted one.
@@ -142,7 +142,7 @@ print('Plotting at t=1')
plotter.plot(pinn, fixed_variables={'t': 1.0})
# The results are not so great, and we can clearly see that as time progress the solution get worse.... Can we do better?
# The results are not so great, and we can clearly see that as time progress the solution gets worse.... Can we do better?
#
# A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:
#
@@ -207,11 +207,11 @@ print('Plotting at t=1')
plotter.plot(pinn, fixed_variables={'t': 1.0})
# We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when the time evolved. By imposing the initial condition the network is able to correctly solve the problem.
# We can see now that the results are way better! This is due to the fact that previously the network was not learning correctly the initial conditon, leading to a poor solution when time evolved. By imposing the initial condition the network is able to correctly solve the problem.
# ## What's next?
#
# Nice you have completed the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now:
# Congratulations on completing the two dimensional Wave tutorial of **PINA**! There are multiple directions you can go now:
#
# 1. Train the network for longer or with different layer sizes and assert the finaly accuracy
#