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In spin glass models one can remove minimization of free energy by some order parameter. One can consider hierarchy of order parameters. It is possible to divide energy among these parts. We can consider relaxation process in glass system…

Disordered Systems and Neural Networks · Physics 2016-08-31 D. B. Saakian

This lecture deals with glassy dynamics and aging in disordered systems. Special emphasis is put on dynamic mean field theory. In the first part I present some of the systems of interest, in particular spin-glasses, supercooled liquids and…

Disordered Systems and Neural Networks · Physics 2007-05-23 Heinz Horner

The present expository article overviews recent mathematical advances on the Fredrickson--Andersen kinetically constrained spin model in two dimensions. It was introduced in physics as a toy model for recovering the glassy phenomenology in…

Disordered Systems and Neural Networks · Physics 2024-01-31 Ivailo Hartarsky , Fabio Martinelli , Cristina Toninelli

We present an exactly solvable random-subcube model inspired by the structure of hard constraint satisfaction and optimization problems. Our model reproduces the structure of the solution space of the random k-satisfiability and k-coloring…

Computational Complexity · Computer Science 2008-05-23 Thierry Mora , Lenka Zdeborova

Modern generative machine learning models demonstrate surprising ability to create realistic outputs far beyond their training data, such as photorealistic artwork, accurate protein structures, or conversational text. These successes…

Machine Learning · Computer Science 2024-01-17 William Gilpin

Differential equations are a ubiquitous tool to study dynamics, ranging from physical systems to complex systems, where a large number of agents interact through a graph with non-trivial topological features. Data-driven approximations of…

Statistical Mechanics · Physics 2024-04-26 Vaiva Vasiliauskaite , Nino Antulov-Fantulin

Two classes of models of driven disordered systems that exhibit history-dependent dynamics are discussed. The first class incorporates local inertia in the dynamics via nonmonotonic stress transfer between adjacent degrees of freedom. The…

Soft Condensed Matter · Physics 2009-11-11 M. Cristina Marchetti

This paper presents a novel theoretical framework for understanding how diffusion models can learn disentangled representations. Within this framework, we establish identifiability conditions for general disentangled latent variable models,…

We present a data-driven machine-learning approach for modeling space-time socioeconomic dynamics. Through coarse-graining fine-scale observations, our modeling framework simplifies these complex systems to a set of tractable mechanistic…

Machine Learning · Computer Science 2024-07-26 James Koch , Pranab Roy Chowdhury , Heng Wan , Parin Bhaduri , Jim Yoon , Vivek Srikrishnan , W. Brent Daniel

Large language models show a surprising in-context learning ability -- being able to use a prompt to form a prediction for a query, yet without additional training, in stark contrast to old-fashioned supervised learning. Providing a…

Disordered Systems and Neural Networks · Physics 2025-07-25 Yuhao Li , Ruoran Bai , Haiping Huang

Often the current mode coupling theory (MCT) of glass transitions is compared with mean field theories. We explore this possible correspondence. After showing a simple-minded derivation of MCT with some difficulties we give a concise…

Statistical Mechanics · Physics 2009-11-07 Kyozi Kawasaki , Bongsoo Kim

The mean-field theory of Kinetically-Constrained-Models is developed by considering the Fredrickson-Andersen model on the Bethe lattice. Using certain properties of the dynamics observed in actual numerical experiments we derive asymptotic…

Disordered Systems and Neural Networks · Physics 2025-01-20 Gianmarco Perrupato , Tommaso Rizzo

We review the use of kinetically constrained models (KCMs) for the study of dynamics in glassy systems. The characteristic feature of KCMs is that they have trivial, often non-interacting, equilibrium behaviour but interesting slow dynamics…

Statistical Mechanics · Physics 2007-05-23 Felix Ritort , Peter Sollich

Interacting magnetic nanoparticles display a wide variety of magnetic behaviors that are now being gathered in the emerging field of 'supermagnetism.' We have investigated how the out-of-equilibrium dynamics in the disordered superspin…

Disordered Systems and Neural Networks · Physics 2015-06-11 Sawako Nakamae , Caroline Crauste-Thibierge , Denis L'Hote , Eric Vincent , Emmanuelle Dubois , Vincent Dupuis , Regine Perzynski

The space time autoregressive model has been widely applied in science, in areas such as economics, public finance, political science, agricultural economics, environmental studies and transportation analyses. The classical space time…

Applications · Statistics 2019-05-14 Wenqian Wang , Beth Andrews

The paper is devoted to recent advances in stochastic modeling of anomalous kinetic processes observed in dielectric materials which are prominent examples of disordered (complex) systems. Theoretical studies of dynamical properties of…

Statistical Mechanics · Physics 2017-03-21 Aleksander Stanislavsky , Karina Weron

State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of the latent states, which govern the values of the…

Machine Learning · Statistics 2024-12-17 Jiahe Lin , George Michailidis

A disordered system is denominated `annealed' when the interactions themselves may evolve and adjust their values to lower the free energy. The opposite (`quenched') situation when disorder is fixed, is the one relevant for physical…

Disordered Systems and Neural Networks · Physics 2022-03-09 Laura Foini , Jorge Kurchan

The study of spin-glass dynamics, long considered the paradigmatic complex system, has reached important milestones. The availability of single crystals has allowed the experimental measurement of spin-glass coherence lengths of almost…

The evolution of natural languages poses a riddle to any theoretical perspective based on efficiency considerations. If languages are already optimally effective means of organization and communication of thought, why do they change? And if…

Neurons and Cognition · Quantitative Biology 2026-03-20 Hediye Yarahmadi , Kwang Il Ryom , Giuseppe Longobardi , Alessandro Treves