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Input convex neural networks as surrogates in mathematical optimisation
Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research. The …
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Input convex neural networks as surrogates in mathematical optimisation
An efficient mixed-integer linear programming formulation for solving influence diagrams
Influence diagrams represent decision-making problems with interdependencies between random events, decisions, and consequences. …
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An efficient mixed-integer linear programming formulation for solving influence diagrams
Employing Deep Neural Operators for PDE control by decoupling training and optimization
Neural networks have been applied to control problems, typically by combining data, differential equation residuals, and objective …
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Employing Deep Neural Operators for PDE control by decoupling training and optimization
Integrated supervisory control and fixed path speed trajectory generation for hybrid electric ships via convex optimization
Battery-hybrid power source architectures can reduce fuel consumption and emissions for ships with diverse operation profiles. However, …
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Integrated supervisory control and fixed path speed trajectory generation for hybrid electric ships via convex optimization