def experiment(P, opt, expname):
# First run with regular refinement
opt["meshRefinement"] = 1
df_uniform = solveProblem(P, opt)
if WRITE_CSV:
fname = '{}_{}_uniform'.format(opt['id'], expname)
hlp.writeOutputToCsv(df_uniform, opt, fname)
# Second run with adaptive refinement
opt["meshRefinement"] = 2
opt["refinementThreshold"] = 0.9
df_adaptive = solveProblem(P, opt)
if WRITE_CSV:
fname = '{}_{}_adaptive'.format(opt['id'], expname)
hlp.writeOutputToCsv(df_adaptive, opt, fname)
def experiment(P, opt, expname):
# First run with regular refinement
opt["meshRefinement"] = 1
df_uniform = solveProblem(P, opt)
if WRITE_CSV:
fname = '{}_{}_uniform'.format(opt['id'], expname)
hlp.writeOutputToCsv(df_uniform, opt, fname)
def experiment(P, opt, expname):
# First run with regular refinement
opt["meshRefinement"] = 1
df_uniform = solveProblem(P, opt)
if WRITE_CSV:
hlp.writeOutputToCsv(CSV_DIR + opt['id'] + expname + '_uniform.csv', df_uniform)
# Second run with adaptive refinement
opt["meshRefinement"] = 2
opt["refinementThreshold"] = 0.95
opt["plotMesh"] = 1
opt["saveMesh"] = 1
opt["plotSolution"] = 1
df_adaptive = solveProblem(P, opt)
if WRITE_CSV:
hlp.writeOutputToCsv(CSV_DIR + opt['id'] + expname + '_adaptive.csv', df_adaptive)
def experiment(P, opt, expname):
# First run with regular refinement
opt["meshRefinement"] = 1
df_uniform = solveProblem(P, opt)
if WRITE_CSV:
hlp.writeOutputToCsv(df_uniform, opt, opt['id'] + expname + '_uniform',
df_uniform)
def degreeStudy(P, opt, minDegree=1, maxDegree=4, filename=None):
out = []
for deg in range(minDegree, maxDegree + 1):
opt["p"] = deg
opt["q"] = deg
df = solveProblem(P, opt)
out.append(df)
if WRITE_CSV:
fname = '{}_degree_study_{}_deg_{}'.format(opt['id'], filename,
deg)
hlp.writeOutputToCsv(df, opt, fname)
return out
# Solve routine
from nonvarFEM import solveProblem
# Auxiliary stuff
import nonvarFEM.helpers as hlp
if __name__ == "__main__":
# Load standard options
global_opt = hlp.standardOptions()
P = FNS_5_4_L_inf_Coeffs()
global_opt["id"] = "FNS_5_4"
global_opt["NdofsThreshold"] = 30000
# Fix polynomial degree
global_opt["p"] = 2
global_opt["q"] = 2
global_opt["plotMesh"] = 1
global_opt["saveMesh"] = 1
global_opt["plotSolution"] = 1
global_opt["saveSolution"] = 1
global_opt["meshRefinement"] = 2
global_opt["refinementThreshold"] = .90
global_opt["writeToCsv"] = 0
# Determine method to estimate error norms
global_opt["errorEstimationMethod"] = 1
df = solveProblem(P, hlp.opt_Own_CG_0_stab(global_opt))
# P = NVP.FullNVP_1()
# P = NVP.FullNVP_2()
# P = NVP.FullNVP_3_1d()
# Parabolic PDEs
# P = PNVP.PNVP_1_2d()
# P = PNVP.PNVP_2()
# P = PNVP.PNVP_WorstOfTwoAssetsPut()
# P = PNVP.PNVP_1_1d()
# P = PNVP.PNVP_1_3d()
# P = PNVP.PNVP_Degenerate_1()
# Elliptic HJB equations
# P = HJB.MinimumArrivalTime(alpha=0.1, beta=0.1)
# P = HJB.MinimumArrivalTimeRadial(alpha=0.1)
# P = HJB.Mother()
# opt["lambda"] = 1.
# P = HJB.Gallistl_Sueli_1()
# Parabolic HJB equations
# P = PHJB.JensenSmears_1()
# P = PHJB.JensenSmears_2()
# P = PHJB.Mother()
P = PHJB.MinimumArrivalTimeParabolic(corrxy=0.9)
# P = PHJB.Merton()
# P = PHJB.Chen_Forsyth()
from time import time
t1 = time()
df = solveProblem(P, opt)
print('Time for solution: {}'.format(time() - t1))