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Found 6 papers, 3 papers with code
Human-Algorithm Collaborative Bayesian Optimization for Engineering Systems
Our methodology exploits the hypothesis that humans are more efficient at making discrete choices rather than continuous ones and enables experts to influence critical early decisions.
Expert-guided Bayesian Optimisation for Human-in-the-loop Experimental Design of Known Systems
Domain experts often possess valuable physical insights that are overlooked in fully automated decision-making processes such as Bayesian optimisation.
Machine Learning-Assisted Discovery of Novel Reactor Designs
To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation.
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning
The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor.
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes
In this work, we present a RL methodology tailored to efficiently address production scheduling problems in the presence of uncertainty.
Reinforcement Learning for Batch Bioprocess Optimization
In this work, we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network.
Optimization and Control Systems and Control
