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arXiv:2609.17440v1 Announce Type: new Abstract: Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensional...
arXiv:2508.10970v2 Announce Type: replace-cross Abstract: Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and foo...
This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-derived quantities are reformulated as objectives of a multi-objective optimization problem, and the resulting Pareto front is exposed to a domain expert for interactive candidate selection rather than returning a single automated recommendation. The framework is extended in two directions: constrained optimization is addressed by incorporating the posterior probability of satisfying output specification limits as an explicit Pareto objective, computed analytically from the GP posterior distribution; robust optimization is addressed by a Monte Carlo sampling strategy that estimates expected lower-confidence performance over a user-defined variability of input perturbations, capturing performance degradation under likely implementation deviations.
Pharmaceutical process characterization is central to Quality by Design because it defines how variations in process parameters affect the ability to meet product quality specifications, thereby suppo...
arXiv:2606. 19230v1 Announce Type: new Abstract: This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-derived quantities are reformulated as objectives of a multi-objective optimization problem, and the resulting Pareto front is exposed to a domain expert for interactive candidate selection rather than returning a single automated recommendation.
JAREX is a Bayesian active‑learning acquisition function designed for multi‑objective process characterization in pharmaceutical manufacturing. It treats characterization as a joint boundary‑learning problem, adaptively selecting experiments to delineate the joint pass region where multiple quality thresholds are simultaneously met. Benchmarks show that JAREX outperforms factorial design of experiments, space‑filling designs, and greedy strategies, achieving more accurate and sample‑efficient boundary recovery, and halving the number of experiments needed for batched runs while maintaining high accuracy.