The context of the research
In certain engineering contexts, such as the design of wind turbines, it is important for designes to be robust to changes in their environment. The term 'active robustness' describes designs which have some capacity to adapt to their environment, for example by changing pitch of a wind turbine blade. Optimising such a design is a difficult problem becuase evaluating a single design in a particualr environment with specific parameter settings typically requires running an expensive simulation. Bayesian optimistation is an optimisation technique aimed at being as sample efficient as possible, making it a perfect target for research into faster design of actively robust products.
Optimisation problems occur extensively thorughout engineering, machine learning and mathematical modelling. In some cases, querying the objective function is very expensive, most commonly becuase it takes a long time to evaluate. For example, the problem might be to optimize the shape of a wind turbine blade, where in order to evaluate a candidate design we need to run a slow computational fluid dynamics simulation. Many other applications exist however, ranging from tuning hyper-parameters in a neural network to recommending wet lab experiements for a pharmaceutical process. Bayesian optimisation is a global optimisation method which attempts to be as a sample efficient as possible duing the optmisation process.
A further consideration in engineering design is that of the robustness. Products must be deployed into environments where the conditions are unknown and/or subject to change. Iin the case of a wind turbine, the wind speed and direction can change throughout the day and can be gusty. rather than building a static design to suit all environments, 'active robustness' refers to the ability of a design to react to changes in it's environment.
In this project, we propose to apply Bayesian optimisation methods to speed up the process of finding optimal designs which can react to their current anvirenmental conditions. In a standard robust Bayesain optimisation problem we seek to optimise the performance of a design over a range of environmental conditions, for example by taking expectation over these conditions or considering a worst case outcome.
The aims and objectives of the research
1.Develop Bayesian optimisation methodology to aid and speed up the design of actively robust products.
2.test the methodology on standard benchmark problems and real-world design problems.
3. Demonstrate the applicability of the methodology to real-world design problems provided by the external partner.
The novelty of the research methodology
Bayesian optimisation has not yet been applied to the problem of active robustness. Over the course of the PhD we will develop novel surrogate models, acquisition functions and optimisation strategies to select experiments which reveal as much information as possible ablout the optimal actively robust design.
The potential impact, applications and benefts
It is possible that the xternal partner, GE, will integrate our methodology into their in-house optimisation toolkit. This is used by many teams across GE, so our research has the potential to impact many projects.
How the research relates to the remit
The research falls into the EPSRC themes of engineering design and artificial intelligence technologies, with potential links to control engineering and operations research depending on the direction taken. The application to power systmes through GE also relates the project to the themes of energy and wind power.