benchmarkfcns.multifidelity.otlcircuit

benchmarkfcns.multifidelity.otlcircuit(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 OTL circuit function. SCORES = otlcircuit(X) computes the value of the OTL circuit function at point X. multifidelity.otlcircuit accepts a matrix of size M-by-6 and returns a matrix SCORES of size M-by-2. Properties (High-fidelity):

• Dimensions: 6

• Recommended domain: [50, 150] x [25, 70] x [0.5, 3] x [1.2, 2.5] x [0.25, 1.2] x [50, 300]

Mathematical Definition

\[\begin{aligned}\]

V_{b1} &= frac{12 R_{b2}}{R_{b1} + R_{b2}} \ f_{hf}(textbf{x}) &= frac{(V_{b1} + 0.74) beta (R_{c2} + 9)}{beta (R_{c2} + 9) + R_f} + frac{11.35 R_f}{beta (R_{c2} + 9) + R_f} + frac{0.74 R_f beta (R_{c2} + 9)}{(beta (R_{c2} + 9) + R_f) R_{c1}} \ f_{lf}(textbf{x}) &= frac{(V_{b1} + 0.74) beta (R_{c2} + 9)}{beta (R_{c2} + 9) + R_f} + frac{11.35 R_f}{beta (R_{c2} + 9) + R_f} end{aligned}

Visualization

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