The Research Centre of Data-Driven Engineering is a multi-disciplinary centre.

We seamlessly combine computational methods from high-performance computing and machine learning with real-world data from experiments to improve the design of engineering systems. We both develop and apply state-of-the-art data-driven methods based on Artificial Intelligence and Machine learning to optimize the design of engineering systems.

Data-Driven Methods

Reinforcement Learning

An area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximise the notion of cumulative reward.

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Neural Networks

A network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes.

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Research areas

Reinforcement Learning

An area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximise the notion of cumulative reward.

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Statistical Learning

A robust method to infer model parameters and errors with Bayesian methods both in real-time and offline.

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Multi-phase Flows

The simultaneous simulation of different aspects of a physical system or systems and the interactions among them.

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Research

Neural Networks

A network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes.

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Bayesian Methods and Optimization

Such methods provide a direct solution to the problems typically posed in statistical inference.

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Real-time Data Assimilation

The science of combining different sources of information to estimate possible states of a system as it evolves in time.

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