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An effective characterization of chaotic conservative Hamiltonian systems in terms of the curvature associated with a Riemannian metric tensor derived from the structure of the Hamiltonian has been extended to a wide class of potential…

Chaotic Dynamics · Physics 2015-05-18 Yossi Ben Zion , Lawrence Horwitz

The physical origin of the backbendings in the equations of state of finite but not necessarily small systems is studied in the Ising model with fixed magnetization (IMFM) by means of the topological properties of the observable…

Statistical Mechanics · Physics 2009-11-10 F. Gulminelli , J. M. Carmona , Ph. Chomaz , J. Richert , S. Jimenez , V. Regnard

At low temperature a thermodynamic system undergoes a phase transition when a physical parameter passes through a singularity point of the free energy, corresponding to formation of a new order. At high temperature the thermal fluctuations…

Statistical Mechanics · Physics 2014-06-17 Bo-Bo Wei , Shao-Wen Chen , Hoi-Chun Po , Ren-Bao Liu

We study a simplified nonlinear thermoelasticity model on two- and three-dimensional tori. A novel functional involving the Fisher information associated with temperature is introduced, extending the previous one-dimensional approach from…

Analysis of PDEs · Mathematics 2025-09-03 Piotr Michał Bies , Tomasz Cieślak , Mario Fuest , Johannes Lankeit , Boris Muha , Srdan Trifunović

We present a simple scheme to evaluate linear response functions including quantum fluctuation corrections on top of the Gutzwiller approximation. The method is derived for a generic multi-band lattice Hamiltonian without any assumption…

Strongly Correlated Electrons · Physics 2017-03-08 Michele Fabrizio

This monograph deals with adaptive supervised classification, using tools borrowed from statistical mechanics and information theory, stemming from the PACBayesian approach pioneered by David McAllester and applied to a conception of…

Machine Learning · Statistics 2007-12-04 Olivier Catoni

We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temperature response to variation of general system parameters.…

Computational Physics · Physics 2024-05-31 Blake R. Duschatko , Xiang Fu , Cameron Owen , Yu Xie , Albert Musaelian , Tommi Jaakkola , Boris Kozinsky

Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits…

Data Analysis, Statistics and Probability · Physics 2020-02-13 Thanh Dung Le , Rita Noumeir , Huu Luong Quach , Ji Hyung Kim , Jung Ho Kim , Ho Min Kim

The recently reported gate-tunable superconductivity in monolayer WTe$_2$ [Science 362, 922 (2018); Science 362, 926 (2018); Nat. Phys. 20, 269 (2024); PRR 7, 013224 (2025)] exhibits several striking anomalies beyond the standard paradigm,…

Superconductivity · Physics 2026-03-04 F. Yang , G. D. Zhao , Y. Shi , L. Q. Chen

We describe the effects of phase coherence on transport and thermodynamic properties of a disordered conducting network. In analogy with weak-localization correction, we calculate the phase coherence contribution to the magnetic response of…

Mesoscale and Nanoscale Physics · Physics 2017-02-08 Michael Pascaud , Gilles Montambaux

Time-varying covariates in longitudinal studies frequently evolve through reciprocal feedback, undergo role reversal, and reflect unobserved individual heterogeneity. Standard statistical frameworks often assume fixed covariate roles and…

Methodology · Statistics 2026-02-27 Niloofar Ramezani , Pascal Nitiema , Jeffrey R. Wilson

The GENERIC structure allows for a unified treatment of different discrete models of hydrodynamics. We first propose a finite volume Lagrangian discretization of the continuum equations of hydrodynamics through the Voronoi tessellation. We…

Statistical Mechanics · Physics 2007-05-23 Mar Serrano , Pep Español

Bayesian model selection commonly relies on Laplace approximation or the Bayesian Information Criterion (BIC), which assume that the effective model dimension equals the number of parameters. Singular learning theory replaces this…

Machine Learning · Statistics 2026-01-06 Kalyaan Rao

For substitutional alloys, typically refered to as classical discrete systems under constant composition, we theoretically examine the role of hidden structure information on evolution of nonlinearity (i.e., correspondence between a set of…

Statistical Mechanics · Physics 2022-02-25 Koretaka Yuge

We study the scaling and universal behavior of temperature-driven first-order phase transitions in scalar models. These transitions are found to exhibit rich phenomena, though they are controlled by a single complex-conjugate pair of the…

Statistical Mechanics · Physics 2016-06-16 Ning Liang , Fan Zhong

The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source general circulation model of the atmosphere…

Atmospheric and Oceanic Physics · Physics 2016-05-25 Valerio Lucarini , Frank Lunkeit , Francesco Ragone

We consider unsupervised learning methods for characterizing the disordered microscopic structure of supercooled liquids and glasses. Specifically, we perform dimensionality reduction of smooth structural descriptors that describe radial…

Statistical Mechanics · Physics 2022-11-24 Daniele Coslovich , Robert L. Jack , Joris Paret

Recent experimental results: (i) the measurement of the $T \ln T$ specific heat in cuprates and the earlier such results in some heavy fermion compounds, (ii) the measurement of the single-particle scattering rates, (iii) the density…

Strongly Correlated Electrons · Physics 2020-07-15 Chandra M. Varma

Carrying out explicitly the computation in a paradigmatic model of non-interacting systems, the Gaussian Model, we show the existence of a singular point in the probability distribution $P(M)$ of an extensive variable $M$. Interpreting…

Statistical Mechanics · Physics 2014-12-10 Federico Corberi , Giuseppe Gonnella , Antonio Piscitelli

Restricted Boltzmann Machines (RBMs) are typically trained using finite-length Gibbs chains under a fixed sampling temperature. This practice implicitly assumes that the stochastic regime remains valid as the energy landscape evolves during…

Machine Learning · Computer Science 2026-03-04 Görkem Can Süleymanoğlu