Generative modeling, which learns joint probability distribution from data and generates
samples according to it, is an important task in machine learning and artificial intelligence …

We propose a general framework for solving statistical mechanics of systems with finite size.
The approach extends the celebrated variational mean-field approaches using …

We propose a different tensor renormalization group algorithm, anisotropic tensor
renormalization group (ATRG), for lattice models in arbitrary dimensions. The proposed …

We present a unified exact tensor network approach to compute the ground state energy,
identify the optimal configuration, and count the number of solutions for spin glasses. The …

Renormalization group theory of tensor network states provides a powerful tool for studying
quantum many-body problems and a new paradigm for understanding entangled structures …

Motivated by dipolar-coupled artificial spin systems, we present a theoretical study of the
classical J 1-J 2-J 3 Ising antiferromagnet on the kagome lattice. We establish the ground …

By using extensive tensor network calculations, we map out the phase diagram of the
frustrated J 1-J 2 Ising model on the square lattice. In particular, we focus on the cases with …

We report on the experimental and theoretical studies of cooling field (H FC) and
temperature (T) dependent exchange bias (EB) in Fe x Au1− x/Fe19Ni81 spin glass …

We have proposed an efficient algorithm to calculate physical quantities in the translational
invariant three-dimensional tensor networks, which is particularly relevant to the study of the …

We propose an improved tensor renormalization-group (TRG) algorithm, the bond-weighted
TRG (BTRG). In BTRG, we generalize the conventional TRG by introducing bond weights on …