benchmarkfcns.multifidelity.currin

benchmarkfcns.multifidelity.currin(arg0: Annotated[numpy.typing.NDArray[numpy.float64], '[m, n]', 'flags.c_contiguous']) → Annotated[numpy.typing.NDArray[numpy.float64], '[m, n]']

Computes the value of the multi-fidelity Currin function. SCORES = currin(X) computes the value of the Currin function at point X. multifidelity.currin accepts a matrix of size M-by-2 and returns a matrix SCORES of size M-by-2. Properties (High-fidelity):

• Dimensions: 2

• Recommended domain: [0, 1]^2

Mathematical Definition

\[\begin{aligned}\]

f(x_1, x_2) &= [1 - exp(-frac{1}{2x_2})] frac{2300x_1^3 + 1900x_1^2 + 2092x_1 + 60}{100x_1^3 + 500x_1^2 + 4x_1 + 20} \ f_{hf}(mathbf{x}) &= f(x_1, x_2) \ f_{lf}(mathbf{x}) &= frac{1}{4} [f(x_1+0.05, x_2+0.05) + f(x_1+0.05, x_2-0.05) + f(x_1-0.05, x_2+0.05) + f(x_1-0.05, x_2-0.05)] end{aligned}

Visualization

No visualization available for this function.