setup_wing_weight_benchmark

pyapprox.benchmarks.setup_wing_weight_benchmark()

Setup the wing weight model benchmark.

The model is given by

$$f(x)=0.036S_w^{0.758}{W}_{fw}^{0.0035}{\left(\frac{A}{{\cos }^2(\mathrm{\Lambda })}\right)}^{0.6}{q}^{0.006}{\lambda }^{0.04}{\left(\frac{100t_c}{\cos (\mathrm{\Lambda })}\right)}^{-0.3}(N_z{W}_{dg}{)}^{0.49}+S_w{W}_p,$$

Returns:

benchmarkBenchmark

Object containing the benchmark attributes documented below

funcallable

The wing weight model with signature

fun(z) -> np.ndarray

where z is a 2D np.ndarray with shape (nvars,nsamples) and the output is a 2D np.ndarray with shape (nsamples,1)

jaccallable

The jacobian of fun with signature

jac(z) -> np.ndarray

where z is a 2D np.ndarray with shape (nvars,nsamples) and the output is a 2D np.ndarray with shape (nvars,1)

variableIndependentMarginalsVariable

Object containing information of the joint density of the inputs z which is the tensor product of independent and identically distributed uniform variables`.

References

[Moon2012]

Moon, H., Dean, A. M., & Santner, T. J. (2012). Two-stage sensitivity-based group screening in computer experiments. Technometrics, 54(4), 376-387.