Abstract
We have investigated the density of states of the Edwards-Anderson model and derived an approximation formula which agrees well with the results of numerical experiments. It is important that the formula can well approximate not only the density of states, but also its first and second derivatives, which are most valuable for obtaining the critical parameters of the system. The evaluations can be further used for examining the behavior of 2D Ising models at different temperatures, particularly for tackling Bayesian inference problems and learning algorithms.
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Malsagov, M.Y., Karandashev, I.M., Kryzhanovsky, B.V. (2019). Approximation of Edwards-Anderson Spin-Glass Model Density of States. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_18
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DOI: https://doi.org/10.1007/978-3-030-22808-8_18
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