June 22, 2026
Y206,Yukawa building
Asia/Tokyo timezone

Contribution List

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  1. Cenke Xu
    6/22/26, 10:00 AM
  2. Sho Araki (Osaka university)
    6/22/26, 1:00 PM
  3. Dr Akira Matsumoto (Graduate School of Science, Osaka Metropolitan University)
    6/22/26, 2:00 PM

    We numerically study the Fidkowski-Kitaev (FK) model using the density matrix renormalization group (DMRG), a tensor network method in the Hamiltonian formalism.
    The 4-fermi interaction of the FK model can generate a mass gap without symmetry-breaking fermion condensation.
    Thus, it is expected that low-energy modes of one chirality will be decoupled from those of the other by a large mass...

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  4. Tatsuya Yamaoka (The University of Osaka)
    6/22/26, 3:00 PM
  5. Tatsuhiro Misumi (Kindai University)
    6/22/26, 4:30 PM

    Physics-Informed Neural Networks (PINNs) have emerged as powerful tools for solving differential equations by incorporating physical constraints directly into the loss function. In this talk, I explore the potential of PINNs in theoretical physics, ranging from non-linear PDEs to lattice field theory. The main focus of this talk is the application of machine learning to lattice fermions. I...

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