Published for SISSA by Springer Received: December 21, 2020 Accepted: March 29, 2021 Published: May 3, 2021 Moduli-dependent Calabi-Yau and SU(3)-structure metrics from machine learning JHEP05(2021)013 Lara B. Anderson,a Mathis Gerdes,b James Gray,a Sven Krippendorf,b Nikhil Raghurama and Fabian Ruehlec,d aDepartment of Physics, Robeson Hall, Virginia Tech, Blacksburg, VA 24061, U.S.A. bArnold Sommerfeld Center for Theoretical Physics, Ludwig-Maximilians-Universität, Theresienstr. 37, 80333 Munich, Germany cCERN, Theoretical Physics Department 1, Esplanade des Particules, Geneva 23, CH-1211, Switzerland dRudolf Peierls Centre for Theoretical Physics, University of Oxford, Parks Road, Oxford OX1 3PU, U.K. E-mail:
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[email protected] Abstract: We use machine learning to approximate Calabi-Yau and SU(3)-structure met- rics, including for the first time complex structure moduli dependence. Our new methods furthermore improve existing numerical approximations in terms of accuracy and speed. Knowing these metrics has numerous applications, ranging from computations of crucial aspects of the effective field theory of string compactifications such as the canonical nor- malizations for Yukawa couplings, and the massive string spectrum which plays a crucial role in swampland conjectures, to mirror symmetry and the SYZ conjecture. In the case of SU(3) structure, our machine learning approach allows us to engineer metrics with certain torsion properties. Our methods are demonstrated for Calabi-Yau and SU(3)-structure manifolds based on a one-parameter family of quintic hypersurfaces in P4.