| Fidelity | Computational cost | Accuracy | Typical use case | |----------|------------------|----------|------------------| | F1 | Very low | Low | Large-scale exploration | | F3 | Medium | Medium | Local refinement | | F5 | High | High | Final solution verification |
At each adaptation step, the link computes: l2hforadaptivity ef f1 f3 f5 link
# Optional blending def blend(self, x, ef): w1 = 1.0 / (1.0 + ef**2) w5 = 1.0 - w1 w3 = 0.5 * (w1 + w5) return w1*self.f1(x) + w3*self.f3(x) + w5*self.f5(x) Most multi-fidelity methods use continuous fidelity parameters (e.g., a value in [0,1]). The discrete but non-consecutive choice (F1, F3, F5) introduces nonlinearity and prevents over-smooth transitions, which can be beneficial in chaotic or highly dynamic environments. | Fidelity | Computational cost | Accuracy |
: EF acts as the primary driver. High EF triggers higher-fidelity evaluation (F5), while low EF allows low-fidelity approximation (F1). 2.2. F1, F3, F5 – Multi-Fidelity Levels Multi-fidelity optimization uses cheaper, lower-accuracy models (F1) to explore, and expensive, high-accuracy models (F5) to exploit. The missing F2 and F4 are intentionally skipped to create distinct gaps, forcing non-linear adaptation. High EF triggers higher-fidelity evaluation (F5), while low
is a design principle where low-level signal processing (L2) feeds into a hierarchical decision layer (H). The keyword fragment l2hforadaptivity suggests a framework specifically built for adaptivity, using a chain of components labeled ef , f1 , f3 , f5 , and a link .