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Out of equilibrium dynamics of repulsive ranked diffusions: the expanding crystal

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arxiv 2301.08552 v1 pith:SN5UA4EG submitted 2023-01-20 cond-mat.stat-mech math-phmath.MP

classification cond-mat.stat-mechmath-phmath.MP
keywords crystaltimedynamicallargeparticlesapproachburgersdynamics
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abstract

We study the non-equilibrium Langevin dynamics of $N$ particles in one dimension with Coulomb repulsive linear interactions. This is a dynamical version of the so-called jellium model (without confinement) also known as ranked diffusion. Using a mapping to the Lieb-Liniger model of quantum bosons, we obtain an exact formula for the joint distribution of the positions of the $N$ particles at time $t$, all starting from the origin. A saddle point analysis shows that the system converges at large time to a linearly expanding crystal. Properly rescaled, this dynamical state resembles the equilibrium crystal in a time dependent effective quadratic potential. This analogy allows to study the fluctuations around the perfect crystal, which, to leading order, are Gaussian. There are however deviations from this Gaussian behavior, which embody long-range correlations of purely dynamical origin, characterized by the higher order cumulants of, e.g., the gaps between the particles, that we calculate exactly. We complement these results using a recent approach by one of us in terms of a noisy Burgers equation. In the large $N$ limit, the mean density of the gas can be obtained at any time from the solution of a deterministic viscous Burgers equation. This approach provides a quantitative description of the dense regime at shorter times. Our predictions are in good agreement with numerical simulations for finite and large $N$.

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Cited by 2 Pith papers

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  1. The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition

    cs.AI 2025-01 conditional novelty 4.0 of 10

    Words that strongly activate both a lower-layer neuron and its strongly connected upper-layer neuron in GPT-2XL form more semantically similar clusters, which the paper interprets as a clipping process.

  2. How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neurons

    q-bio.NC 2024-12 reject novelty 2.0 of 10

    The paper names three components of a neuron's aggregation function as cognitive factors and reports near-unity correlations in GPT-2XL, but the effects are largely true by construction.

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