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Adaptive Framework for Data-Driven Probabilistic Design Space Identification
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DOI：10.1016/j.ces.2026.124255.png)
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Abstract
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• Design space identification for systems without reliable mechanistic models • Feasibility-informed sampling guides efficient exploration of feasible regions • Modified Delaunay triangulation method enables adaptive, geometry-driven sampling • Deterministic strategy yields consistent and reproducible design space estimates • Reduces experimentation and avoids variability seen in batch Bayesian optimization
Keywords:
Quality by Design
Design Space Identification
Adaptive Sampling
Design of Experiments
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