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Generative AI has achieved remarkable empirical success, but from the perspective of statistics it often remains opaque: its predictions may be accurate, yet the underlying mechanism is difficult to interpret, analyze, and trust. This book…

Machine Learning · Statistics 2026-03-11 Shinto Eguchi

Normalizing flows are a class of generative models that enable exact likelihood evaluation. While these models have already found various applications in particle physics, normalizing flows are not flexible enough to model many of the…

High Energy Physics - Phenomenology · Physics 2022-09-07 Rob Verheyen

Exact string solutions are presented, providing backgrounds where a dynamical change of topology is occuring. This is induced by the time variation of a modulus field. Some lessons are drawn concerning the region of validity of effective…

High Energy Physics - Theory · Physics 2009-10-28 E. Kiritsis , C. Kounnas

A new, frequency modulation mechanism for zonal flow pattern formation is presented. The model predicts the probability distribution function of the flow strength as well as the evolution of the characteristic spatial scale. Magnetic…

Plasma Physics · Physics 2016-09-28 Z. B. Guo , P. H. Diamond

We introduce a special class of real semiflows, which is used to define a general type of evolution semigroups, associated to not necessarily exponentially bounded evolution families. Giving spectral characterizations of the corresponding…

Classical Analysis and ODEs · Mathematics 2023-03-29 Nicolae Lupa , Liviu Horia Popescu

Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for entertainment to solving inverse problems. Nonetheless, training a generator is a non-trivial feat…

Machine Learning · Computer Science 2025-03-07 Eldad Haber , Shadab Ahamed , Md. Shahriar Rahim Siddiqui , Niloufar Zakariaei , Moshe Eliasof

We study string formation and dynamics in a scalar field theory with a global $U(1)$ symmetry. If a scalar field $\Phi$ is initially displaced from the minimum of a wine-bottle potential, even if uniformly over large spatial patches, small…

High Energy Physics - Phenomenology · Physics 2025-08-15 Michael A. Fedderke , Junwu Huang , Nils Siemonsen

We apply generative models to a key problem in the string compactification program, namely construction of type IIB string vacua. To this end, we make use of a Bayesian Flow Network, a generative model capable of handling discrete data, to…

High Energy Physics - Theory · Physics 2026-01-13 Moritz Walden , Magdalena Larfors

Flow Matching (FM) is a recent generative modelling technique: we aim to learn how to sample from distribution $\mathfrak{X}_1$ by flowing samples from some distribution $\mathfrak{X}_0$ that is easy to sample from. The key trick is that…

Differential Geometry · Mathematics 2025-10-24 Finn M. Sherry , Bart M. N. Smets

We investigate the phenomenon of producing vibration-induced rotational motion in a cylinder filled with achiral rods and being vibrated by an external drive. The arrangement of the rods develops chirality as a result of the interaction of…

Statistical Mechanics · Physics 2022-04-29 Satyanu Bhadra , Shankar Ghosh , Shamik Gupta

Based on the recent success of the \angantyr model in describing multiplicity distributions of the hadronic final state in high energy heavy ion collisions, we investigate how far one can go with a such a string-based scenario to describe…

High Energy Physics - Phenomenology · Physics 2021-04-01 Christian Bierlich , Smita Chakraborty , Gösta Gustafson , Leif Lönnblad

Denoising-based models, including diffusion and flow matching, have led to substantial advances in graph generation. Despite this progress, such models remain constrained by two fundamental limitations: a computational cost that scales…

Machine Learning · Computer Science 2026-04-02 Yoann Boget , Pablo Strasser , Alexandros Kalousis

Generative diffusion models showed high success in many fields with a powerful theoretical background. They convert the data distribution to noise and remove the noise back to obtain a similar distribution. Many existing reviews focused on…

Machine Learning · Computer Science 2024-09-19 Melike Nur Yeğin , Mehmet Fatih Amasyalı

Flow matching has achieved remarkable success, yet the factors influencing the quality of its generation process remain poorly understood. In this work, we adopt a denoising perspective and design a framework to empirically probe the…

Computer Vision and Pattern Recognition · Computer Science 2025-12-22 Anne Gagneux , Ségolène Martin , Rémi Gribonval , Mathurin Massias

Hard spheres in Newtonian fluids serve as paradigms for Non-Newtonian materials phenomena exhibited by colloidal suspensions. A recent experimental study (Cheng et al. 2011 Science, 333, 1276) showed that upon application of shear to such a…

Soft Condensed Matter · Physics 2013-10-07 Xinliang Xu , Stuart A Rice , Aaron R Dinner

We present non-supersymmetric toroidal compactifications of type I string theory with both constant background NSNS two-form flux and non-trivial magnetic flux on the various D9-branes. The non-vanishing B-flux admits four-dimensional…

High Energy Physics - Theory · Physics 2009-10-31 Ralph Blumenhagen , Boris Kors , Dieter Lust

We present a new general method to construct an action functional for a non-potential field theory. The key idea relies on representing the governing equations of the theory relative to a diffeomorphic flow of curvilinear coordinates which…

Mathematical Physics · Physics 2015-03-19 Daniele Venturi

Evolution of structure functions and their moments at low and moderate $Q^2$ is studied in the chiral field theory. Evolution equations based on perturbation expansion in the coupling constant of the effective theory are derived and solved…

High Energy Physics - Phenomenology · Physics 2016-09-06 Kakali Ray-Maity , Padmanabha Dasgupta

Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this…

Machine Learning · Statistics 2018-07-11 Diederik P. Kingma , Prafulla Dhariwal

We study in this work the 2D dynamics of an experimental system of disk-shaped rotors, fluidized by turbulent upflow. Contrary to previous knowledge, our experiments show the same particle chiral geometry can produce flows with different…