mailitics

Category: cond-mat.stat-mech

  • Riemannian Stochastic Interpolants for Amorphous Particle Systems

    Riemannian Stochastic Interpolants for Amorphous Particle Systems arXiv:2512.16607v1 Announce Type: new Abstract: Modern generative models hold great promise for accelerating diverse tasks involving the simulation of physical systems, but they must be adapted to the specific constraints of each domain. Significant progress has been made for biomolecules and crystalline materials. Here, we address amorphous materials…

  • PCA recovery thresholds in low-rank matrix inference with sparse noise

    PCA recovery thresholds in low-rank matrix inference with sparse noise arXiv:2511.11927v1 Announce Type: new Abstract: We study the high-dimensional inference of a rank-one signal corrupted by sparse noise. The noise is modelled as the adjacency matrix of a weighted undirected graph with finite average connectivity in the large size limit. Using the replica method from…

  • Graphical model for tensor factorization by sparse sampling

    Graphical model for tensor factorization by sparse sampling arXiv:2510.17886v1 Announce Type: new Abstract: We consider tensor factorizations based on sparse measurements of the tensor components. The measurements are designed in a way that the underlying graph of interactions is a random graph. The setup will be useful in cases where a substantial amount of data…

  • Bayesian symbolic regression: Automated equation discovery from a physicists’ perspective

    Bayesian symbolic regression: Automated equation discovery from a physicists’ perspective arXiv:2507.19540v1 Announce Type: new Abstract: Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approaches, rely on heuristic model selection criteria, heuristic regularization, and heuristic exploration of model space. Here, we…

  • Coupled Entropy: A Goldilocks Generalization?

    Coupled Entropy: A Goldilocks Generalization? arXiv:2506.17229v1 Announce Type: new Abstract: Nonextensive Statistical Mechanics (NSM) has developed into a powerful toolset for modeling and analyzing complex systems. Despite its many successes, a puzzle arose early in its development. The constraints on the Tsallis entropy are in the form of an escort distribution with elements proportional to…

  • Resonances in reflective Hamiltonian Monte Carlo

    Resonances in reflective Hamiltonian Monte Carlo arXiv:2504.12374v1 Announce Type: new Abstract: In high dimensions, reflective Hamiltonian Monte Carlo with inexact reflections exhibits slow mixing when the particle ensemble is initialised from a Dirac delta distribution and the uniform distribution is targeted. By quantifying the instantaneous non-uniformity of the distribution with the Sinkhorn divergence, we elucidate…

  • Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions

    Feature learning from non-Gaussian inputs: the case of Independent Component Analysis in high dimensions arXiv:2503.23896v1 Announce Type: new Abstract: Deep neural networks learn structured features from complex, non-Gaussian inputs, but the mechanisms behind this process remain poorly understood. Our work is motivated by the observation that the first-layer filters learnt by deep convolutional neural networks…