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The Paper

Hysteresis, Avalanches, and Noise: Numerical Methods

Matthew C. Kuntz, Olga Perkovic, Karin A. Dahmen,
Bruce W. Roberts, and James P. Sethna


In studying the avalanches and noise in a model of hysteresis loops, we have developed two relatively straightforward algorithms which have allowed us to study large systems efficiently. Our model is the random-field Ising model at zero temperature, with deterministic albeit random dynamics. The first algorithm, implemented using sorted lists, scales in computer time as O(N log N), and asymptotically uses N*sizeof(double)+N*sizeof(int) bytes of memory. The second algorithm, which never generates the random fields, scales in time as O(N log N) and asymptotically needs storage of only one bit per spin, about 96 times less memory than the first algorithm. We present results for system sizes of up to a billion spins, which can be run on a workstation with 128MB of RAM in a few hours. We also show that important physical questions were resolved only with the largest of these simulations.

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Statistical Mechanics: Entropy, Order Parameters, and Complexity, now available at Oxford University Press (USA, Europe).