DATA-CENTRIC: Developing AccounTAble Computational ENgineering Through Robust InferenCe

Lead Research Organisation: University of Liverpool
Department Name: Civil Engineering and Industrial Design


DATA-CENTRIC will fundamentally transform modern computational engineering through the development of algorithms that are accountable. This means algorithms capable of quantifying the uncertainty arising from computation itself, delivering simulations that are more transparent, traceable and at the same time more efficient. Crucial decisions in science, engineering, healthcare and public policy rely on established methodologies such as the Finite Element Method and the Stochastic Finite Element Method. However, the models that inform such decisions suffer from an inevitable loss of accuracy due to, and not limited to the following sources of uncertainty: a) time and cost constraints of running modern high-fidelity computer models, b) simplifying approximations necessary to translate mathematical models into computational models, and c) limited numerical precision inherent to any computer system. Therefore, there is a continuous risk of relying on unverified computational evidence, and the path from modelling to decision-making can be (inadvertently or unwillingly) obscured by the lack of accountability.
DATA-CENTIC will solve this problem through Probabilistic Numerics, a framework that will enable decision-makers to monitor, diagnose and control the quality of computer simulations. Probabilistic Numerics treats computation as a statistical problem, thus enriching computation with a probabilistic measure of numerical error. This idea is gathering momentum, especially in the UK. However, theoretical development are still in their early stages and except for a few examples, it has not been applied to solve large-scale industrial problems. Consequently, it has not yet been adopted by industry. DATA-CENTRIC will bridge this gap. . The proposed approach will provide radically new insights into the Finite Element Method and the Stochastic Finite Element Method. In particular, it will produce new solutions to industrial problems in Biomechanics and Robust Design. This has the potential of transforming personalised medicine and high-value manufacturing and will open the door to new industrial applications.

Planned Impact

Within the ongoing data revolution, computer models have become omnipresent and affect virtually every aspect of economic, social and scientific activity. It is now the norm to make critical decisions on the basis of computations that rely on complex models and vast amounts of data. In this context, the proposed research will benefit any decision-maker who relies on complex models to inform decisions. Given the wide range of application of engineering computations, in particular deterministic and stochastic Finite Element models, this Fellowship will actively engage with industry to deliver impact in Biomechanics and Robust Design. The impact in Biomechanics has the potential to advance the quest for truly personalised medicine by using clinical and imaging data to produce accountable computational models of biological tissue and organs. This will have scientific impact as it will integrate and quantify the high degree of variability in geometric and material properties and numerical uncertainty. It will also have societal impact, since accountable models will better inform decisions taken by surgeons and manufacturing companies for diagnostic visualisation, surgical planning and the design of implants and surgical equipment. The impact in Robust Design, particularly in the aerospace industry, will allow for the quantification of uncertainty that propagates through the analysis from the design stage to the manufacturing stage, thus delivering transparency in the transition from research to innovation.
The Fellowship will not be constrained to the aforementioned industries. The theoretical developments and industrial experience will be disseminated, through a one-day workshop, to small, medium and large-sized companies, government bodies and charities. Tailored tutorials will be delivered on-site to R&D departments of industrial partners. A three-day Study Group with industry will ensure that the wider community of industrial partners and academics can engage in a discussion, develop solutions in the framework of the proposed research and identify best practice. A webinar will be recorded such that the research is accessible to industrial and academic stakeholders, as well as the general public.


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Hristov P (2019) Adaptive Gaussian process emulators for efficient reliability analysis in Applied Mathematical Modelling

Related Projects

Project Reference Relationship Related To Start End Award Value
EP/S001476/1 01/06/2018 31/08/2019 £387,228
EP/S001476/2 Transfer EP/S001476/1 01/09/2019 31/05/2021 £244,313
Description One of the central uncertainty quantification tasks within this award is rare-event simulation, which is used to estimate probabilities of failure for complex physical systems. This estimation depends on running computationally expensive models used in industrial settings. We have published a paper on how to mitigate the cost of simulating rare events when the computational cost of the underlying model is prohibitively high. The latter is done through a statistical approximation of the computer model. We have also provided strategies to select the model input configurations such that the cost mitigation is robust and efficient. The paper is:

Adaptive Gaussian process emulators for efficient reliability analysis (2019) Applied Mathematical Modelling, 71, pp 138-151.

A second paper was published. It was a contributed discussion on a probabilsitic numerical algorithm. The reference is:

Contributed Discussion of "A Bayesian Conjugate Gradient Method" (2019) Bayesian Analysis, 14 (3), pp 937-1012.
Exploitation Route The rare-event simulation framework that we are developing can be seamlessly applied to solve three problems: (1) model parameter updating (Bayesian inference); (2) calibration of expensive computer models; (3) estimation of probabilities of failure. These three problems are fundamental in science and industry. The practical applications range from designing more resilient engineering systems, building computational models that are better calibrated with experimental data and hence are more reliable, and making robust inference in statistical modelling.
Sectors Aerospace, Defence and Marine,Energy,Environment

Description DSTL - PDRA Statistical Analysis
Amount £94,234 (GBP)
Organisation Defence Science & Technology Laboratory (DSTL) 
Sector Public
Country United Kingdom
Start 01/2019 
End 11/2020