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While dependence on SymPy was dropped in #37 for computing basis functions, it might be useful for preprocessing certain examples. It would not then be a dependence for the scikit-fem package, just for the examples.
One is suggested by the FEniCS tutorial example ft05_nonlinear_poisson.py. The example is basically like docs/examples/ex10.py, except that it adds an artificial right-hand side to balance the equation for a specified solution; i.e. it uses the method of manufactured solutions. That right-hand side is computed from the stipulated exact solution using SymPy.
sympy.utilities.lambdify to evaluate symbolic vector fields on points specified by a numpy.ndarray like Mesh.p
There are a few examples (ex12, ex13, ex14, ex16, ex17, ex18, ex19, ex20) which involve exact solutions; I wonder whether it would be worth modifying one or more of them to use SymPy to compute the exact solution to show how it's done here.
The text was updated successfully, but these errors were encountered:
According to Cellier & Ruyer-Quil (2019), Scikit-FDiff uses SymPy to allow ‘easy and automated finite difference discretization’; might be worth a look for inspiration or comparison.
Cellier, N. & Ruyer-Quil, C. (2019). scikit-finite-diff, a new tool for PDE solving. Journal of Open Source Software, 4, 1356. doi:10.21105/joss.01356
While dependence on SymPy was dropped in #37 for computing basis functions, it might be useful for preprocessing certain examples. It would not then be a dependence for the scikit-fem package, just for the examples.
One is suggested by the FEniCS tutorial example ft05_nonlinear_poisson.py. The example is basically like
docs/examples/ex10.py
, except that it adds an artificial right-hand side to balance the equation for a specified solution; i.e. it uses the method of manufactured solutions. That right-hand side is computed from the stipulated exact solution using SymPy.A translation of the FEniCS example into scikit-fem is at https://github.com/gdmcbain/fenics-tuto-in-skfem/tree/master/05_poisson_nonlinear. It goes beyond the original in using:
numpy.ndarray
likeMesh.p
There are a few examples (ex12, ex13, ex14, ex16, ex17, ex18, ex19, ex20) which involve exact solutions; I wonder whether it would be worth modifying one or more of them to use SymPy to compute the exact solution to show how it's done here.
The text was updated successfully, but these errors were encountered: