Designing actively robust products with Bayesian Optimisation

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.

In the 2018 Government Office for Science report, 'Computational Modelling: Technological Futures', Greg Clarke, the Secretary of State for Business Energy and Industrial Strategy, wrote "Computational modelling is essential to our future productivity and competitiveness, for businesses of all sizes and across all sectors of the economy". With its focus on computational models, the mathematics that underpin them, and their integration with complex data, the MathSys II CDT will generate diverse impacts beyond academia. This includes impacts on skills, on the economy, on policy and on society.

Impacts on skills.
MathSys II will produce a minimum of 50 PhD graduates to support the growing national demand for advanced mathematical modelling and data analysis skills. The CDT will provide each of them with broad core skills in the MSc, a deep knowledge of their chosen research specialisation in the PhD and a complementary qualification in transferable skills integrated throughout. Graduates will thus acquire the profiles needed to form the next generation of leaders in business, government and academia. They will be supported by an integrated pastoral support framework, including a diverse group of accessible leadership role models. The cohort based environment of the CDT provides a multiplier effect by encouraging cohorts to forge long-lasting professional networks whose value and influence will long outlast the CDT itself. MathSys II will seek to maximise the influence of these networks by providing topical training in Responsible Research and Innovation, by maintaining a robust Equality, Diversity & Inclusion policy, and by integration with Warwick's global network of international partnerships.

Economic impacts.
The research outputs from many MathSys II PhD projects will be of direct economic value to commercial, public sector and charitable external partners. Engagement with CDT partners will facilitate these impacts. This includes co-supervision of PhD and MSc projects, co-creation of Research Study Groups, and a strong commitment to provide placements/internships for CDT students. When commercial innovations or IP are generated, we will work with Warwick Ventures, the commercial arm of the University of Warwick, to commercialise/license IP where appropriate. Economic impact may also come from the creation of new companies by CDT graduates. MathSys II will present entrepreneurship as a viable career option to students. One external partner, Spectra Analytics, was founded by graduates of the preceding Complexity Science CDT, thus providing accessible role models. We will also provide in-house entrepreneurship training via Warwick Ventures and host events by external start-up accelerator Entrepreneur First.

Impacts on policy.
The CDT will influence policy at the national and international level by working with external partners operating in policy. UK examples include Department of Health, Public Health England and DEFRA. International examples include World Health Organisation (WHO) and the European Commission for the Control of Foot-and-mouth Disease (EuFMD). MathSys students will also utilise the recently announced UKRI policy internships scheme.

Impacts on society.
Public engagement will allow CDT students to promote the value of their research to society at large. Aside from social media, suitable local events include DataBeers, Cafe Scientifique, and the Big Bang Fair. MathSys will also promote a socially-oriented ethos of technology for the common good. Concretely, this includes the creation of open-source software, integration of software and data carpentry into our computational and data driven research training and championing open-access to research. We will also contribute to the 'innovation culture and science' strand of Coventry's 2021 City of Culture programme.