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Details of Grant 

EPSRC Reference: EP/T016280/1
Title: Cohomology, Machine Learning and String Model Building
Principal Investigator: Constantin, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Virginia Polytechnic Inst & State Uni
Department: Oxford Physics
Organisation: University of Oxford
Scheme: EPSRC Fellowship
Starts: 01 October 2020 Ends: 30 September 2024 Value (£): 435,734
EPSRC Research Topic Classifications:
Artificial Intelligence Fundamentals of Computing
Mathematical Physics
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
03 Dec 2019 Stephen Hawking Fellowship Announced
21 Jan 2020 Stephen Hawking Fellowship Interview Panel 2 Announced
Summary on Grant Application Form
The proposed research capitalizes on a newly discovered class of mathematical formulae underpinning the realisation of string theory solutions that unify all known forms of matter and forces. String theory has had a profound impact on the development of both mathematics and physics and, more recently, on the construction of machine learning algorithms.

String theory is a high-energy, extra-dimensional and supersymmetric theory, in which many ideas about physics beyond the Standard Model can be incorporated in a natural way. Although difficult, making contact with experimental physics is an imperative for string theory, requiring a sustained effort in developing the existing models up to the point where they can communicate with experimental results such as the LHC data. The difficulty is not conceptual, but rather mathematical and computational in nature. String theory is geometrical par excellence and, as such, one needs to identify the specific geometry that reduces it to the Standard Model of particle physics at low energies.

The project contributes in an essential way to the resolution of this problem. It uses experimental mathematics derived from string theory to uncover and understand new algebraic and geometric structures. The new structures feed back into string theory, providing unexpected shortcuts to incredibly hard computations. It is rare to find a new type of mathematical structure that has so much potential for problem solving. This interplay between mathematics and physics is characteristic to string theory and has crucially contributed to making it the principal driving force in fundamental particle physics.

Machine learning techniques have seen a wide range of applications in numerous areas of science and in industry. String theory and, more broadly, physics require a qualitatively different kind of machine learning, focused not only on results, but also on uncovering the mechanisms underlying them. The proposal goes beyond the standard 'black box' approach that gives correct results but no explanations by using machine lerning for the formulation of mathematically precise conjectures that can subsequently be approached using methods of algebraic geometry, everything converging towards the ultimate goal of understanding the physical implications of string theory.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.ox.ac.uk