Source code for pymordemos.burgers_ei

#!/usr/bin/env python
# This file is part of the pyMOR project (
# Copyright 2013-2018 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (

"""Burgers with EI demo.

Model order reduction of a two-dimensional Burgers-type equation
(see pymor.analyticalproblems.burgers) using the reduced basis method
with empirical operator interpolation.


  EXP_MIN       Minimal exponent

  EXP_MAX       Maximal exponent

  EI_SNAPSHOTS  Number of snapshots for empirical interpolation.

  EISIZE        Number of interpolation DOFs.

  SNAPSHOTS     Number of snapshots for basis generation.

  RBSIZE        Size of the reduced basis

  --cache-region=REGION           Name of cache region to use for caching solution snapshots
                                  (NONE, MEMORY, DISK, PERSISTENT)
                                  [default: DISK]

  --ei-alg=ALG                    Interpolation algorithm to use (EI_GREEDY, DEIM)
                                  [default: EI_GREEDY].

  --grid=NI                       Use grid with (2*NI)*NI elements [default: 60].

  --grid-type=TYPE                Type of grid to use (rect, tria) [default: rect].

  --initial-data=TYPE             Select the initial data (sin, bump) [default: sin]

  --lxf-lambda=VALUE              Parameter lambda in Lax-Friedrichs flux [default: 1].

  --not-periodic                  Solve with dirichlet boundary conditions on left
                                  and bottom boundary.

  --nt=COUNT                      Number of time steps [default: 100].

  --num-flux=FLUX                 Numerical flux to use (lax_friedrichs, engquist_osher)
                                  [default: engquist_osher].

  -h, --help                      Show this message.

  -p, --plot-err                  Plot error.

  --plot-ei-err                   Plot empirical interpolation error.

  --plot-error-landscape          Calculate and show plot of reduction error vs. basis sizes.

  --plot-error-landscape-N=COUNT  Number of basis sizes to test [default: 10]

  --plot-error-landscape-M=COUNT  Number of collateral basis sizes to test [default: 10]

  --plot-solutions                Plot some example solutions.

  --test=COUNT                    Use COUNT snapshots for stochastic error estimation
                                  [default: 10].

  --vx=XSPEED                     Speed in x-direction [default: 1].

  --vy=YSPEED                     Speed in y-direction [default: 1].

  --ipython-engines=COUNT         If positive, the number of IPython cluster engines to use
                                  for parallel greedy search. If zero, no parallelization
                                  is performed. [default: 0]

  --ipython-profile=PROFILE       IPython profile to use for parallelization.

import sys
import math as m
import time

import numpy as np
from docopt import docopt

from pymor.algorithms.greedy import greedy
from pymor.algorithms.ei import interpolate_operators
from pymor.analyticalproblems.burgers import burgers_problem_2d
from pymor.discretizers.fv import discretize_instationary_fv
from pymor.grids.rect import RectGrid
from pymor.grids.tria import TriaGrid
from pymor.parallel.default import new_parallel_pool
from pymor.reductors.basic import GenericRBReductor

[docs]def main(args): args = docopt(__doc__, args) args['--cache-region'] = args['--cache-region'].lower() args['--ei-alg'] = args['--ei-alg'].lower() assert args['--ei-alg'] in ('ei_greedy', 'deim') args['--grid'] = int(args['--grid']) args['--grid-type'] = args['--grid-type'].lower() assert args['--grid-type'] in ('rect', 'tria') args['--initial-data'] = args['--initial-data'].lower() assert args['--initial-data'] in ('sin', 'bump') args['--lxf-lambda'] = float(args['--lxf-lambda']) args['--nt'] = int(args['--nt']) args['--not-periodic'] = bool(args['--not-periodic']) args['--num-flux'] = args['--num-flux'].lower() assert args['--num-flux'] in ('lax_friedrichs', 'engquist_osher') args['--plot-error-landscape-N'] = int(args['--plot-error-landscape-N']) args['--plot-error-landscape-M'] = int(args['--plot-error-landscape-M']) args['--test'] = int(args['--test']) args['--vx'] = float(args['--vx']) args['--vy'] = float(args['--vy']) args['--ipython-engines'] = int(args['--ipython-engines']) args['EXP_MIN'] = int(args['EXP_MIN']) args['EXP_MAX'] = int(args['EXP_MAX']) args['EI_SNAPSHOTS'] = int(args['EI_SNAPSHOTS']) args['EISIZE'] = int(args['EISIZE']) args['SNAPSHOTS'] = int(args['SNAPSHOTS']) args['RBSIZE'] = int(args['RBSIZE']) print('Setup Problem ...') problem = burgers_problem_2d(vx=args['--vx'], vy=args['--vy'], initial_data_type=args['--initial-data'], parameter_range=(args['EXP_MIN'], args['EXP_MAX']), torus=not args['--not-periodic']) print('Discretize ...') if args['--grid-type'] == 'rect': args['--grid'] *= 1. / m.sqrt(2) d, _ = discretize_instationary_fv( problem, diameter=1. / args['--grid'], grid_type=RectGrid if args['--grid-type'] == 'rect' else TriaGrid, num_flux=args['--num-flux'], lxf_lambda=args['--lxf-lambda'], nt=args['--nt'] ) if args['--cache-region'] != 'none': d.enable_caching(args['--cache-region']) print(d.operator.grid) print('The parameter type is {}'.format(d.parameter_type)) if args['--plot-solutions']: print('Showing some solutions') Us = () legend = () for mu in d.parameter_space.sample_uniformly(4): print('Solving for exponent = {} ... '.format(mu['exponent'])) sys.stdout.flush() Us = Us + (d.solve(mu),) legend = legend + ('exponent: {}'.format(mu['exponent']),) d.visualize(Us, legend=legend, title='Detailed Solutions', block=True) pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'], ipython_profile=args['--ipython-profile']) ei_d, ei_data = interpolate_operators(d, ['operator'], d.parameter_space.sample_uniformly(args['EI_SNAPSHOTS']), # NOQA error_norm=d.l2_norm, product=d.l2_product, max_interpolation_dofs=args['EISIZE'], alg=args['--ei-alg'], pool=pool) if args['--plot-ei-err']: print('Showing some EI errors') ERRs = () legend = () for mu in d.parameter_space.sample_randomly(2): print('Solving for exponent = \n{} ... '.format(mu['exponent'])) sys.stdout.flush() U = d.solve(mu) U_EI = ei_d.solve(mu) ERR = U - U_EI ERRs = ERRs + (ERR,) legend = legend + ('exponent: {}'.format(mu['exponent']),) print('Error: {}'.format(np.max(d.l2_norm(ERR)))) d.visualize(ERRs, legend=legend, title='EI Errors', separate_colorbars=True) print('Showing interpolation DOFs ...') U = np.zeros(U.dim) dofs = ei_d.operator.interpolation_dofs U[dofs] = np.arange(1, len(dofs) + 1) U[ei_d.operator.source_dofs] += int(len(dofs)/2) d.visualize(d.solution_space.make_array(U), title='Interpolation DOFs') print('RB generation ...') reductor = GenericRBReductor(ei_d) greedy_data = greedy(d, reductor, d.parameter_space.sample_uniformly(args['SNAPSHOTS']), use_estimator=False, error_norm=lambda U: np.max(d.l2_norm(U)), extension_params={'method': 'pod'}, max_extensions=args['RBSIZE'], pool=pool) rd = greedy_data['rd'] print('\nSearching for maximum error on random snapshots ...') tic = time.time() mus = d.parameter_space.sample_randomly(args['--test']) def error_analysis(N, M): print('N = {}, M = {}: '.format(N, M), end='') rd = reductor.reduce(N) rd = rd.with_(operator=rd.operator.with_cb_dim(M)) l2_err_max = -1 mumax = None for mu in mus: print('.', end='') sys.stdout.flush() u = rd.solve(mu) URB = reductor.reconstruct(u) U = d.solve(mu) l2_err = np.max(d.l2_norm(U - URB)) l2_err = np.inf if not np.isfinite(l2_err) else l2_err if l2_err > l2_err_max: l2_err_max = l2_err mumax = mu print() return l2_err_max, mumax error_analysis = np.frompyfunc(error_analysis, 2, 2) real_rb_size = len(reductor.RB) real_cb_size = len(ei_data['basis']) if args['--plot-error-landscape']: N_count = min(real_rb_size - 1, args['--plot-error-landscape-N']) M_count = min(real_cb_size - 1, args['--plot-error-landscape-M']) Ns = np.linspace(1, real_rb_size, N_count).astype( Ms = np.linspace(1, real_cb_size, M_count).astype( else: Ns = np.array([real_rb_size]) Ms = np.array([real_cb_size]) N_grid, M_grid = np.meshgrid(Ns, Ms) errs, err_mus = error_analysis(N_grid, M_grid) errs = errs.astype(np.float) l2_err_max = errs[-1, -1] mumax = err_mus[-1, -1] toc = time.time() t_est = toc - tic print(''' *** RESULTS *** Problem: parameter range: ({args[EXP_MIN]}, {args[EXP_MAX]}) h: sqrt(2)/{args[--grid]} grid-type: {args[--grid-type]} initial-data: {args[--initial-data]} lxf-lambda: {args[--lxf-lambda]} nt: {args[--nt]} not-periodic: {args[--not-periodic]} num-flux: {args[--num-flux]} (vx, vy): ({args[--vx]}, {args[--vy]}) Greedy basis generation: number of ei-snapshots: {args[EI_SNAPSHOTS]} prescribed collateral basis size: {args[EISIZE]} actual collateral basis size: {real_cb_size} number of snapshots: {args[SNAPSHOTS]} prescribed basis size: {args[RBSIZE]} actual basis size: {real_rb_size} elapsed time: {greedy_data[time]} Stochastic error estimation: number of samples: {args[--test]} maximal L2-error: {l2_err_max} (mu = {mumax}) elapsed time: {t_est} '''.format(**locals())) sys.stdout.flush() if args['--plot-error-landscape']: import matplotlib.pyplot as plt import mpl_toolkits.mplot3d # NOQA fig = plt.figure() ax = fig.add_subplot(111, projection='3d') # we have to rescale the errors since matplotlib does not support logarithmic scales on 3d plots # surf = ax.plot_surface(M_grid, N_grid, np.log(np.minimum(errs, 1)) / np.log(10), rstride=1, cstride=1, cmap='jet') if args['--plot-err']: U = d.solve(mumax) URB = reductor.reconstruct(rd.solve(mumax)) d.visualize((U, URB, U - URB), legend=('Detailed Solution', 'Reduced Solution', 'Error'), title='Maximum Error Solution', separate_colorbars=True) return ei_data, greedy_data
if __name__ == '__main__': main(sys.argv[1:])