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.
Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address...
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...
By Niki Triantafyllou, Andrea Bernardi, Maria M. Papathanasiou
arXiv:2508.10970v2 Announce Type: replace-cross
Abstract: Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and foo...
By Adrian Martens, Mathias Neufang, Alessandro Butt\'e, Moritz von Stosch, Antonio del Rio Chanona, Laura Marie Helleckes
arXiv:2607. 18308v1 Announce Type: cross Abstract: Calibration of grey-box simulation models is a constrained optimization problem in which model evaluations are expensive, the parameter space can be high-dimensional, and the search must respect plausibility constraints.
By David G\'omez-Guill\'en, Mireia Diaz, Josep Lluis Arcos, Jes\'us Cerquides
arXiv:2603. 24567v2 Announce Type: replace-cross Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions.
By Raju Chowdhury, Tanmay Sen, Biswabrata Pradhan
tidyHEBO is a BoTorch-native Bayesian optimization tool that jointly applies Yeo-Johnson output warping to a Gaussian‑process surrogate, evaluates acquisition functions on the original objective scale, and conducts constrained cumulative Pareto search across multiple acquisition criteria. Using only default settings, it outperformed other methods on the Olympus benchmark and performed strongly on synthetic, Needle‑in‑a‑Haystack, and Bayesmark tasks, while adaptive batching offered a trade‑off between parallelization and optimization quality. These results position tidyHEBO as a robust, reproducible optimizer suitable for diverse practical problems, including scientific applications and hyperparameter tuning.
By L. A. Zhukov, E. V. Shaburova, D. V. Antonets
arXiv:2502. 18966v2 Announce Type: replace Abstract: General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and high-throughput experimentation.
By Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Al\'an Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff
arXiv:2608. 11483v1 Announce Type: new Abstract: Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints.
By Kelvin P. Idanwekhai, Enes Kelestemur, Benjamin Strickland, Matthew Hart, Steini Davidsson, Angelos Angelopoulos, Ron Alterovitz, Marcello DeLuca, Alexander Tropsha
arXiv:2607. 23480v1 Announce Type: new Abstract: Variational autoencoders (VAEs) transform high-dimensional, often noisy data into a compact latent representation, making downstream optimization more tractable.
By Ye Shi
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.
By Xinyang Li, Kevin Stone, Ajit Vikram
arXiv:2608. 00641v2 Announce Type: replace Abstract: Bayesian optimization (BO) relies on a surrogate model and an acquisition function, yet the most suitable choices vary across tasks and optimization stages.
By Changquan Zhao, Yuxiang Sun, Ruihao Zhu, Cheng Hua, Yulian He