1 citations · 1 across the 3 of their papers we have counts for
4 papers
Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling
Matthew Marsh, Benoit Chachuat, Antonio del Rio Chanona
Machine learning models are increasingly used to model chemical process systems, yet they often lack principled uncertainty quantification and mechanisms to enforce physical constr…
Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation
Julius H Ramlau, Friedrich Hastedt, Tolga Birdal +3
Designing spray nozzles requires predicting how geometry shapes transient two-phase breakup, but high-fidelity volume-of-fluid (VOF) simulations with adaptive mesh refinement (AMR)…
Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems
Tom Savage, Ehecatl Antonio del Rio Chanona
Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation. In this article we apply hig…
Hierarchical planning-scheduling-control -- Optimality surrogates and derivative-free optimization
Damien van de Berg, Nilay Shah, Ehecatl Antonio del Rio-Chanona
Planning, scheduling, and control typically constitute separate decision-making units within chemical companies. Traditionally, their integration is modelled sequentially, but rece…