50 citations · 344 across the 40 of their papers we have counts for
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Stable and Efficient Algorithms for the Fermion Determinant
Johann Ostmeyer
Some algorithms for the numerically exact treatment of fermion determinants are summarised. This is not supposed to be a review, rather a concise handbook. The audience is expected…
Stable Determinant Monte Carlo Simulations at Large Inverse Temperature
Thomas Luu, Johann Ostmeyer, Petar Sinilkov +1
At low temperatures where the naïve implementation of determinant quantum Monte Carlo (DQMC) methods suffers from loss of precision and numerical instabilities when…
Exponential speed up in Monte Carlo sampling through Radial Updates
Johann Ostmeyer
Recently, it has been shown that the hybrid Monte Carlo (HMC) algorithm is guaranteed to converge exponentially to a given target probability distribution o…
Mitigating the Hubbard Sign Problem with Complex-Valued Neural Networks
Marcel Rodekamp, Evan Berkowitz, Christoph Gäntgen +3
Monte Carlo simulations away from half-filling suffer from a sign problem that can be reduced by deforming the contour of integration. Such a transformation, which induces a Jacobi…
The Hubbard model with fermionic tensor networks
Manuel Schneider, Johann Ostmeyer, Karl Jansen +2
Many electromagnetic properties of graphene can be described by the Hubbard model on a honeycomb lattice. However, this system suffers strongly from the sign problem if a chemical…
Simulating both parity sectors of the Hubbard Model with Tensor Networks
Manuel Schneider, Johann Ostmeyer, Karl Jansen +2
Tensor networks are a powerful tool to simulate a variety of different physical models, including those that suffer from the sign problem in Monte Carlo simulations. The Hubbard mo…
The Ising Model with Hybrid Monte Carlo
Johann Ostmeyer, Evan Berkowitz, Thomas Luu +2
The Ising model is a simple statistical model for ferromagnetism. There are analytic solutions for low dimensions and very efficient Monte Carlo methods, such as cluster algorithms…