5 papers
Input convex neural networks as surrogates in mathematical optimisation
Yu Liu, Jan Kronqvist, Fabricio Oliveira
Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research. The prevailing approach uses feedforward neural netw…
A robust optimization approach to flow decomposition
Moritz Stinzendörfer, Philine Schiewe, Fabricio Oliveira
In this paper, we generalize the minimum flow decomposition problem (MFD) to incorporate uncertain edge capacities and tackle it from the perspective of robust optimization. In the…
ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
Yu Liu, Fabricio Oliveira, Jan Kronqvist
Two-stage stochastic programming (2SP) offers a basic framework for modelling decision-making under uncertainty, yet scalability remains a challenge due to the computational comple…
Employing Deep Neural Operators for PDE control by decoupling training and optimization
Oliver G. S. Lundqvist, Fabricio Oliveira
Neural networks have been applied to control problems, typically by combining data, differential equation residuals, and objective costs in the training loss or by incorporating au…
Employing Federated Learning for Training Autonomous HVAC Systems
Fredrik Hagström, Vikas Garg, Fabricio Oliveira
Buildings account for 40% of global energy consumption. A considerable portion of building energy consumption stems from heating, ventilation, and air conditioning (HVAC), and thus…