Tag: Uncertainty Quantification
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Interval Filters for Pre-Selection in Model-Assisted Constrained Pareto Optimization
Michael Emmerich, JYU, Finland, 28.1.2026 When objective and constraint evaluations are expensive (CFD/FEM, digital-twin simulations, etc.), we often rely on Gaussian process regression (Kriging) as a surrogate. A GP does not only predict a mean vector, it also delivers uncertainty. Interpreted component-wise, this uncertainty naturally forms an axis-aligned confidence box in for objectives (and similarly…
Michael Emmerich 