def setup_sm(sm_name, settings={}):
xt, yt, xlimits = get_rans_crm_wing()
_tmp = __import__('smt', globals(), locals(), ['surrogate_models'], 0)
interp = getattr(_tmp.surrogate_models, sm_name)(**settings)
interp.set_training_values(xt, yt)
with Silence():
interp.train()
return xt, yt, interp
def test_ny(self):
xt, yt, _ = get_rans_crm_wing()
interp = QP()
interp.set_training_values(xt, yt)
interp.train()
v0 = np.zeros((4, 2))
for ix, i in enumerate([10, 11, 12, 13]):
v0[ix, :] = interp.predict_values(np.atleast_2d(xt[i, :]))
v1 = interp.predict_values(np.atleast_2d(xt[10:14, :]))
expected_diff = np.zeros((4, 2))
np.testing.assert_allclose(v1 - v0, expected_diff, atol=1e-15)
def setupCRM(LF_candidate="QP", Bridge_candidate="KRG", type_bridge="Additive"):
xt, yt, _ = get_rans_crm_wing()
optionsLF = {}
optionsB = {}
M = VFM(
type_bridge=type_bridge,
name_model_LF=LF_candidate,
name_model_bridge=Bridge_candidate,
X_LF=xt,
y_LF=yt,
X_HF=xt,
y_HF=yt,
options_LF=optionsLF,
options_bridge=optionsB,
)
return M, xt
from smt.surrogate_models import RMTC
from smt.examples.rans_crm_wing.rans_crm_wing import (
get_rans_crm_wing,
plot_rans_crm_wing,
)
xt, yt, xlimits = get_rans_crm_wing()
interp = RMTC(
num_elements=20, xlimits=xlimits, nonlinear_maxiter=100, energy_weight=1e-10
)
interp.set_training_values(xt, yt)
interp.train()
plot_rans_crm_wing(xt, yt, xlimits, interp)