Artificial intelligence (AI), machine learning (ML), and deep learning (DL) provide a broad range of powerful tools that can be used to tackle a wide variety of problems in theoretical high-energy physics. The two-day (mini) SIMIS-APCTP workshop on “AI Methods in Theoretical Physics” will bring together theoretical physics researchers whose current work focuses on the application and development of AI methods in high-energy theoretical physics. Indicative topics include the identification and classification of geometric structures in string theory (such as Calabi–Yau manifolds and quantum geometries), the reconstruction of holographic spacetimes from boundary data, the study of strongly coupled quantum field theories, the exploration of fundamental structures and observables in quantum and conformal field theories (such as scattering amplitudes), as well as the solution of key equations in theoretical physics (such as the Bethe and Yang–Baxter equations).
Invited speakers
Robert de Mello Koch (Huzhou U.)
Xin Gao (Sichuan U.)
Song He (Ningbo U.)
Yuji Hirono (Tsukuba U.)
Keun-Young Kim (GIST)
Shailesh Lal (BIMSA)
Seung-Joo Lee (Yonsei U.) *
Rak-Kyeong Seong (UNIST)
Norihiro Tanahashi (Kyoto U.)
Jiahua Tian (East China Normal U.)
Ashutosh Tripathi (APCTP)
Lingxiao Wang (RIKEN) *
Shao-Feng Wu (Shanghai U.)
Yuan Xin (SIMIS)
Yang Zhang (USTC Hefei)
* To be confirmed
Organizers
George Linardopoulos (SIMIS)
Zhesen Yang (APCTP)
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