| EPSRC Reference: |
EP/S001476/2 |
| Title: |
DATA-CENTRIC: Developing AccounTAble Computational ENgineering Through Robust InferenCe |
| Principal Investigator: |
Diaz De la O, Dr F |
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| Department: |
Mathematics |
| Organisation: |
UCL |
| Scheme: |
EPSRC Fellowship - NHFP |
| Starts: |
01 September 2019 |
Ends: |
31 May 2021 |
Value (£): |
244,313
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| EPSRC Research Topic Classifications: |
| Fundamentals of Computing |
Numerical Analysis |
| Statistics & Appl. Probability |
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Summary on Grant Application Form |
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.
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Key Findings |
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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