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The FoundLatt project aims to develop a machine learning model for lattice Quantum Chromodynamics, enhancing predictions in particle physics. By creating a reusable foundation model, it seeks to improve the efficiency of calculations and reduce systematic uncertainties in theoretical predictions.
Despite describing fundamental physics with spectacular accuracy, the Standard Model of particle physics is known to be incomplete.
As experimental programmes probe its boundaries at higher energies and with better precision, it is critical that measurements are faced with fully controlled theory predictions.
Lattice simulations of Quantum Chromodynamics (QCD) form a major pillar of this effort.
However, even with increasing computing capacity, many lattice QCD calculations will not be possible…
THE UNIVERSITY OF EDINBURGH
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