2 citations · 3 across the 6 of their papers we have counts for
6 papers
DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery
Roberto Aliaga Medina, Paulina Quintanilla, Antonio del Rio Chanona
Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological process…
A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development
Samuel Stricker, Claus Wirnsperger, Alessandro Butté +4
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-der…
From Feasible to Practical: Pareto-Optimal Synthesis Planning
Friedrich Hastedt, Dongda Zhang, Antonio del Rio Chanona
Current computer-aided synthesis planning (CASP) methods often treat retrosynthesis as solved once a single feasible route is identified, focusing primarily on convergence or short…
Multi-fidelity batch Bayesian optimization for bioprocess development across scales
Adrian Martens, Mathias Neufang, Alessandro Butté +3
Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and food. However, developing high-performing…
Robust Market Potential Assessment: Designing optimal policies for low-carbon technology adoption in an increasingly uncertain world
Tom Savage, Antonio del Rio Chanona, Gbemi Oluleye
Increasing the adoption of alternative technologies is vital to ensure a successful transition to net-zero emissions in the manufacturing sector. Yet there is no model to analyse t…
Distributional constrained reinforcement learning for supply chain optimization
Jaime Sabal Bermúdez, Antonio del Rio Chanona, Calvin Tsay
This work studies reinforcement learning (RL) in the context of multi-period supply chains subject to constraints, e.g., on production and inventory. We introduce Distributional Co…