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We discuss the distributed matching scheme in accelerators where control of transverse beam phase space, oscillation, and transport is accomplished by flexible distribution of focusing elements beyond dedicated matching sections. Besides…

Accelerator Physics · Physics 2018-08-07 Yu-Chiu Chao

In recent years, Rectified flow (RF) has gained considerable popularity largely due to its generation efficiency and state-of-the-art performance. In this paper, we investigate the degree to which RF automatically adapts to the intrinsic…

Machine Learning · Statistics 2026-02-24 Saptarshi Roy , Alessandro Rinaldo , Purnamrita Sarkar

We consider the problem of density estimation on Riemannian manifolds. Density estimation on manifolds has many applications in fluid-mechanics, optics and plasma physics and it appears often when dealing with angular variables (such as…

Machine Learning · Statistics 2016-11-10 Mevlana C. Gemici , Danilo Rezende , Shakir Mohamed

Replacing positivity constraints by an entropy barrier is popular to approximate solutions of linear programs. In the special case of the optimal transport problem, this technique dates back to the early work of Schr\"odinger. This approach…

Analysis of PDEs · Mathematics 2017-01-10 Guillaume Carlier , Vincent Duval , Gabriel Peyré , Bernhard Schmitzer

This chapter concerns "control volume analysis", the standard engineering tool for the analysis of flow systems, and its application to entropy balance calculations. Firstly, the principles of control volume analysis are enunciated and…

Fluid Dynamics · Physics 2014-07-22 Robert K. Niven , Bernd R. Noack

Entropy is a classical measure to quantify the amount of information or complexity of a system. Various entropy-based measures such as functional and spectral entropies have been proposed in brain network analysis. However, they are less…

Neurons and Cognition · Quantitative Biology 2018-03-08 Hyekyoung Lee , Eunkyung Kim , Hyejin Kang , Youngmin Huh , Youngjo Lee , Seonhee Lim , Dong Soo Lee

Sampling from constrained distributions has a wide range of applications, including in Bayesian optimization and robotics. Prior work establishes convergence and feasibility guarantees for constrained sampling, but assumes that the feasible…

Machine Learning · Computer Science 2026-05-13 Cornelius V. Braun , Tilman Burghoff , Marc Toussaint

Given the growth in the variety and precision of astronomical datasets of interest for cosmology, the best cosmological constraints are invariably obtained by combining data from different experiments. At the likelihood level, one…

Cosmology and Nongalactic Astrophysics · Physics 2024-09-04 Arrykrishna Mootoovaloo , Carlos García-García , David Alonso , Jaime Ruiz-Zapatero

Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emittance ratios are critical for future linear colliders.…

A cascade model is described based on multiplier distributions determined from 3D direct numerical simulations (DNS) of turbulent particle laden flows, which include two-way coupling between the phases at global mass loadings equal to…

Astrophysics · Physics 2015-06-30 Robert C. Hogan , Jeffrey N. Cuzzi

We report on an extended version of the one-dimensional model proposed by Constant \emph{et al.} [Phys. Rev. Lett 82(8), 1668 (1999)] to study phase matching of high-order harmonic generation in absorbing and dispersive medium. The model -…

Optics · Physics 2022-01-07 Balázs Major , Katalin Varjú

Normalizing Flows explicitly maximize a full-dimensional likelihood on the training data. However, real data is typically only supported on a lower-dimensional manifold leading the model to expend significant compute on modeling noise.…

Machine Learning · Computer Science 2024-06-28 Peter Sorrenson , Felix Draxler , Armand Rousselot , Sander Hummerich , Lea Zimmermann , Ullrich Köthe

In this letter we propose the use of physics techniques for entropy determination on constrained parameter optimization problems. The main feature of such techniques, the construction of an unbiased walk on energy space, suggests their use…

Statistical Mechanics · Physics 2009-11-07 A. R. Lima , M. Argollo de Menezes

We investigate the distribution of entanglement entropy in hybrid quantum circuits consisting of random unitary gates and local measurements applied at a finite rate. We demonstrate that higher moments of the entanglement entropy…

Quantum Physics · Physics 2026-04-14 Jeonghyeok Park , Hyukjoon Kwon , Hyunseok Jeong

Experimental characterization of blood flow in living organisms is crucial for understanding the development and function of cardiovascular systems, but there have been no techniques reported for snapshot imaging of thick samples in large…

Collider events with multi-stage cascade decays fill out the kinematically allowed region in phase space with a density that is enhanced at the boundary. The boundary encodes all available information about the spectrum and is well…

High Energy Physics - Phenomenology · Physics 2017-03-08 Baris Altunkaynak , Can Kilic , Matthew D. Klimek

Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate available partial observations and additional priors. In…

Precise modeling of extended sources is a central challenge in modern optical engineering, laser physics, and computational lithography. Unlike ideal point sources or completely incoherent thermal radiation sources, real-world light sources…

Optics · Physics 2025-12-09 Rongqi Shang , Donglin Ma

Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions with high flexibility and expressiveness. In this work, we introduce a new type…

Machine Learning · Computer Science 2023-06-08 Jonas Köhler , Michele Invernizzi , Pim de Haan , Frank Noé

Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sampling inefficiency…