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_pod.py
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183 lines (148 loc) · 5.38 KB
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# -*- coding: utf-8 -*-
"""
@author: Olav M.S. Gran
"""
from itertools import product
import numpy as np
from scipy.linalg import eigh, fractional_matrix_power
from .exceptions import SolutionMatrixIsZeroCanNotComputePODError
# le2d is a LinearElasticity2dProblem, not imported due to circular import
# rb_data is ReducedOrderData, not imported due to circular import
# from ._linear_elasticity_2d_problem_class import LinearElasticity2DProblem
# from ._rb_data_class import ReducedOrderData
def get_vec_from_range(range_, m, mode):
"""
Get vector of m uniform or Gauss-Lobatto points from range_
Parameters
----------
range_ : tuple
the range of numbers to consider.
m : int
number of points in vector.
mode : str
sampling mode, uniform or Gauss-Lobatto.
Raises
------
NotImplementedError
if mode is not uniform or Gauss-Lobatto.
Returns
-------
np.array
array of sampling points.
"""
if mode.lower() == "uniform":
return np.linspace(range_[0], range_[1], m)
elif mode.lower() == "gauss-lobatto":
from quadpy.c1 import gauss_lobatto
return 0.5 * ((range_[1] - range_[0]) * gauss_lobatto(m).points + (range_[1] + range_[0]))
else:
raise NotImplementedError(
f"Mode {mode} is not implemented. The implemented modes are uniform and gauss lobatto.")
def make_solution_matrix(ns, e_young_vec, nu_poisson_vec, le2d):
"""
Parameters
----------
ns : int
number of snapshots.
e_young_vec : TYPE
array of young's modules.
nu_poisson_vec : np.array
array of poisson ratios.
le2d :
the solver.
Raises
------
SolutionMatrixIsZeroCanNotComputePODError
If all values is the snapshot matrix s_mat are zero.
Returns
-------
s_mat : np.array
snapshot matrix.
"""
s_mat = np.zeros((le2d.n_free, ns))
i = 0
# solve system for all combinations of (e_young, nu_poisson)
for (e_young, nu_poisson) in product(e_young_vec, nu_poisson_vec):
le2d.hfsolve(e_young, nu_poisson, print_info=False)
s_mat[:, i] = le2d.uh_free
i += 1
if (s_mat == 0).all():
error_text = "Solution matrix is zero, can not compute POD for building a reduced model. " \
+ "The most likely cause is f_func=0, dirichlet_bc_func=0 and neumann_bc_func=0, " \
+ "where two last may be None."
raise SolutionMatrixIsZeroCanNotComputePODError(error_text)
return s_mat
def pod_with_energy_norm(le2d, rb_data):
"""
Proper orthogonal decomposition with respect to the energy norm
Parameters
----------
le2d :
the solver.
rb_data :
reduced-order data.
Returns
-------
None.
"""
e_young_vec = get_vec_from_range(rb_data.e_young_range, rb_data.rb_grid[0], rb_data.pod_sampling_mode)
nu_poisson_vec = get_vec_from_range(rb_data.nu_poisson_range, rb_data.rb_grid[1], rb_data.pod_sampling_mode)
e_mean = np.mean(rb_data.e_young_range)
nu_mean = np.mean(rb_data.nu_poisson_range)
rb_data.s_mat = make_solution_matrix(rb_data.ns_rom, e_young_vec, nu_poisson_vec, le2d)
a_free = le2d.compute_a_free(e_mean, nu_mean)
if rb_data.ns_rom <= le2d.n_free:
# build correlation matrix
corr_mat = rb_data.s_mat.T @ a_free @ rb_data.s_mat
# find the eigenvalues and eigenvectors of it
sigma2_vec, z_mat = eigh(corr_mat)
# reverse arrays because they are in ascending order
rb_data.sigma2_vec = sigma2_vec[::-1]
rb_data.z_mat = z_mat[:, ::-1]
else:
rb_data.x05 = fractional_matrix_power(a_free.A, 0.5)
# build correlation matrix
corr_mat = rb_data.x05 @ rb_data.s_mat @ rb_data.s_mat.T @ rb_data.x05
# find the eigenvalues and eigenvectors of it
sigma2_vec, z_mat = eigh(corr_mat)
# reverse arrays because they are in ascending order
rb_data.sigma2_vec = sigma2_vec[::-1]
rb_data.z_mat = z_mat[:, ::-1]
# compute n_rom from relative information content
i_n = np.cumsum(rb_data.sigma2_vec) / np.sum(rb_data.sigma2_vec)
rb_data.n_rom = np.min(np.argwhere(i_n >= 1 - rb_data.eps_pod ** 2)) + 1
def compute_v(n_rom, n_free, rb_data):
"""
Compute the matrix V
Parameters
----------
n_rom : int
our chosen "reduced-order degrees of freedom" ("n_rom"),
can be set to different from n_rom-true.
n_free : int
the high-fidelity degrees of freedom.
rb_data :
reduced-order data.
Returns
-------
None.
"""
if rb_data.ns_rom <= n_free:
rb_data.v = rb_data.s_mat @ rb_data.z_mat[:, :n_rom] / np.sqrt(rb_data.sigma2_vec[:n_rom])
else:
rb_data.v = np.linalg.solve(rb_data.x05, rb_data.z_mat[:, :n_rom])
def get_e_young_nu_poisson_mat(rb_data):
"""
Get the matrix of all combinations of (e_young, nu_piosson)
Parameters
----------
rb_data :
reduced-order data.
Returns
-------
np.array
the matrix of all combinations of (e_young, nu_piosson).
"""
e_young_vec = get_vec_from_range(rb_data.e_young_range, rb_data.rb_grid[0], rb_data.pod_sampling_mode)
nu_poisson_vec = get_vec_from_range(rb_data.nu_poisson_range, rb_data.rb_grid[1], rb_data.pod_sampling_mode)
return np.array(list(product(e_young_vec, nu_poisson_vec)))