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Various methods for leveraging turbulent fluctuation measurements from fusion plasma experiments are introduced, along with selected application examples. These can be categorized into spectral methods, statistical methods, and physics…

Plasma Physics · Physics 2025-10-27 Minjun J. Choi

Obtaining conductance spectra for a concentration of disordered impurities distributed over a nanoscale device with sensing capabilities is a well-defined problem. However, to do this inversely, i.e., extracting information about the…

Mesoscale and Nanoscale Physics · Physics 2021-12-01 S. Mukim , J. O'Brien , M. Abarashi , M. S. Ferreira , C. G. Rocha

We use optimal fluctuation method for a new ballistic $\sigma$-model to study the long time dispersion of conductance $G(t)$ of a mesoscopic sample. In the long time limit the conductance of a $d$-dimensional sample decays as $\exp (-A…

Condensed Matter · Physics 2016-08-31 B. A. Muzykantskii , D. E. Khmelnitskii

Fluctuation theorems, which have been developed over the past 15 years, have resulted in fundamental breakthroughs in our understanding of how irreversibility emerges from reversible dynamics, and have provided new statistical mechanical…

Statistical Mechanics · Physics 2015-05-13 E. M. Sevick , R. Prabhakar , Stephen R. Williams , Debra J. Searles

Diffusion, a fundamental internal mechanism emerging in many physical processes, describes the interaction among different objects. In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data…

Machine Learning · Computer Science 2023-05-02 Tangjun Wang , Zehao Dou , Chenglong Bao , Zuoqiang Shi

We present a theoretical model of matter-wave diffraction through a material nanostructure. This model is based on the numerical solution of the time-dependent Schr{\"o}dinger equation, which goes beyond the standard semi-classical…

We integrate neural operators with diffusion models to address the spectral limitations of neural operators in surrogate modeling of turbulent flows. While neural operators offer computational efficiency, they exhibit deficiencies in…

Machine Learning · Computer Science 2025-02-14 Vivek Oommen , Aniruddha Bora , Zhen Zhang , George Em Karniadakis

Recently, Portelli et al (2003) have semi-numerically obtained a functional form of the probability distribution of fluctuations in the total energy flow in a model for fluid turbulence. This follows earlier work suggesting that…

Statistical Mechanics · Physics 2007-05-23 S. C. Chapman , G. Rowlands , N. W. Watkins

The parametrically pumped Kerr model describes a driven-dissipative nonlinear cavity, whose nonequilibrium phase diagram features both continuous and discontinuous quantum phase transitions. We consider the consequences of these critical…

Quantum Physics · Physics 2022-10-26 Michael J. Kewming , Mark T. Mitchison , Gabriel T. Landi

Reproducible, sample-specific magnetoresistance fluctuations (magnetic fingerprints) have been observed experimentally in the low-temperature part of the superconducting transition regime of disordered superconducting Au_0.7In_0.3…

Superconductivity · Physics 2007-05-23 Yu. Zadorozhny , D. R. Herman , Y. Liu

Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sensor failures and irregular sampling lead to pervasive missing values. However, existing…

Machine Learning · Computer Science 2025-12-18 Runze Li , Hanchen Wang , Wenjie Zhang , Binghao Li , Yu Zhang , Xuemin Lin , Ying Zhang

We analyze the spectral properties of a very general two-channel fermion-boson transport model in the insulating and metallic regimes, and the signatures of the metal-insulator quantum phase transition in between. To this end we determine…

Strongly Correlated Electrons · Physics 2015-05-14 S. Ejima , H. Fehske

Quantum fluctuations in the mazer are considered, arising either from the atomic motion or from the quantized intracavity field. Analytical results, for both the meza and the hyperbolic secant mode profile, predict for example an…

Quantum Physics · Physics 2009-11-13 Jonas Larson

Diffusion is the result of repeated random scattering. It governs a wide range of phenomena from Brownian motion, to heat flow through window panes, neutron flux in fuel rods, dispersion of light in human tissue, and electronic conduction.…

Mesoscale and Nanoscale Physics · Physics 2018-07-04 Zhou Shi , Azriel Z. Genack

This work considers the variable-exponent fractional diffusion-wave equation, which describes, e.g. the propagation of mechanical diffusive waves in viscoelastic media with varying material properties. Rigorous numerical analysis for this…

Numerical Analysis · Mathematics 2025-09-29 Wenlin Qiu , Xiangcheng Zheng

Transport and the approach to equilibrium in interacting classical and quantum systems is a challenging problem of both theoretical and experimental interest. One useful organizing principle characterizing equilibration is the dissipative…

Statistical Mechanics · Physics 2024-08-27 Alexios A. Michailidis , Dmitry A. Abanin , Luca V. Delacrétaz

Deep nonparametric regression, characterized by the utilization of deep neural networks to learn target functions, has emerged as a focus of research attention in recent years. Despite considerable progress in understanding convergence…

Machine Learning · Statistics 2024-08-01 Yuling Jiao , Lican Kang , Jin Liu , Heng Peng , Heng Zuo

We consider the intrinsic fluctuation conductivity in metals with multiply sheeted Fermi surfaces approaching a superconducting critical point. Restricting our attention to extreme type-II multicomponent superconductors motivates focusing…

Superconductivity · Physics 2026-05-05 Sondre Duna Lundemo , Asle Sudbø

This paper is devoted to the statistics of the quantum eigenfunctions in an ensemble of finite disordered systems (metallic grains). We focus on moments of inverse participation ratio. In the universal random matrix limit that corresponds…

Mesoscale and Nanoscale Physics · Physics 2009-10-30 V. Prigodin , B. L. Altshuler

Data attribution for generative models seeks to quantify the influence of individual training examples on model outputs. Existing methods for diffusion models typically require access to model gradients or retraining, limiting their…

Machine Learning · Computer Science 2025-10-17 Yutian Zhao , Chao Du , Xiaosen Zheng , Tianyu Pang , Min Lin