We present a framework for deriving anomaly detection algorithms on timeseries data when the time and expression of anomalous behaviour is unknown. The framework is suited for problems in which individual machine learning ...

We explore the use of deep reinforcement learning to provide strategies for long term scheduling of hydropower production. We consider a use-case where the aim is to optimise the yearly revenue given week-by-week inflows ...

Slender marine structures such as deep-water riser systems are continuously exposed to currents, leading to vortex-induced vibrations (VIV) of the structure. This may result in amplified drag loads and fast accumulation ...

Low complexity of a system model is essential for its use in real-time applications. However, sparse identification methods commonly have stringent requirements that exclude them from being applied in an industrial setting. ...

Background The identification of gene–gene and gene–environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible ...

Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving. We introduce a pseudo-Hamiltonian ...

Power producers use a wide range of decision support systems to manage and plan for sales in the day-ahead electricity market. The available tools have advantages and disadvantages and the operators are often faced with ...