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| self.cov_name = cov_name # name of covariance function to use | ||
| self.cov = getattr(self, "cov_" + cov_name) # select the right function | ||
| self.mean = mean # mean of random field | ||
| # self.rho = rho # correlation length |
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There should be a way of indicate the dimensionality of the GRF
| return str(self) | ||
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| def cov_exp(self, r): | ||
| """ |
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Would it be more convenient to use scikit-learn native kernels instead of implementing ours?
| return self.sigma2 * np.exp(-1 / self.rho * r) | ||
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| def cov_squared_exp(self, r1, r2): | ||
| """ |
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This should be either available for 1-2-3D or accept the dimension as a parameter
| self.pv_nugrf_file = Path(pv_path) / (pv_name + "nugrf.xdmf") | ||
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| def setup(self) -> None: | ||
| self.field_function_space = df.fem.FunctionSpace(self.experiment.mesh, ("CG", 1)) |
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Is the degree 1 compulsory or should it accept any?
src/fenicsxconcrete/finite_element_problem/linear_elasticity_grf.py
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I needed the Gaussian Random Field implementation for my project, so I generated some minimum working code translated from @div-tyg implementation. Currently only 2D GRFs for E and nu in a linear elastic problem are possible, as some parts of the code need work to properly handle any case. It supposes that #108 has been implemented, as I needed it for my use cases. I make some comments on things that I noticed that should be adapted. Additionally I did not prepare any new tests or really considered where the GRF generator should be located, this is supposed to be just a starting point for the final implementation.