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This article investigates the full Boltzmann equation up to second order in the cosmological perturbations. Describing the distribution of polarized radiation by a tensor valued distribution function, we study the gauge dependence of the…

General Relativity and Quantum Cosmology · Physics 2018-06-28 Cyril Pitrou

We introduce a general method to count unlabeled combinatorial structures and to efficiently generate them at random. The approach is based on pointing unlabeled structures in an "unbiased" way that a structure of size n gives rise to n…

Discrete Mathematics · Computer Science 2011-03-29 Manuel Bodirsky , Éric Fusy , Mihyun Kang , Stefan Vigerske

Over the last two decades, it has been argued that the Lorentz transformation mechanism, which imposes the generalization of Newton's classical mechanics into Einstein's special relativity, implies a generalization, or deformation, of the…

Statistics Theory · Mathematics 2022-03-04 G. Kaniadakis

A stationary distribution function that describes the entire processes of propagation of relativistic particles, including the transition between the ballistic and diffusion regimes, is obtained. The spacial component of the constructed…

High Energy Astrophysical Phenomena · Physics 2015-10-14 A. Y. Prosekin , S. R. Kelner , F. A. Aharonian

Flux sampling is an analysis that, based on a distribution, picks randomly an efficient number of points from the solution space of a metabolic model. Unlike most constraint-based analyses, flux sampling does not require an objective…

Molecular Networks · Quantitative Biology 2026-04-01 Haris Zafeiropoulos , Daniel Rios Garza

Scale invariance and the resulting power law behaviours are seen in diverse systems. In this work we consider translation, rotational and scale invariant systems defined on a lattice, such that the variables defining the state at every…

Statistical Mechanics · Physics 2025-05-19 Vaibhav Wasnik

Spreading of nano-droplets is an interesting and technologically relevant phenomenon where thermal fluctuations lead to unexpected deviations from well-known deterministic laws. Here, we apply the newly developed fluctuating non-ideal…

Fluid Dynamics · Physics 2013-12-24 Markus Gross , Fathollah Varnik

A quadrature-based finite-difference lattice Boltzmann model is developed that is suitable for simulating relativistic flows of massless particles. We briefly review the relativistc Boltzmann equation and present our model. The quadrature…

Fluid Dynamics · Physics 2017-09-26 Robert Blaga , Victor E. Ambrus

We study the problem of learning conditional distributions of the form $p(G | \hat G)$, where $G$ and $\hat G$ are two 3D graphs, using continuous normalizing flows. We derive a semi-equivariance condition on the flow which ensures that…

Machine Learning · Computer Science 2023-04-17 Eyal Rozenberg , Daniel Freedman

On a finite graph, there is a natural family of Boltzmann probability measures on cycle-rooted spanning forests, parametrized by weights on cycles. For a certain subclass of those weights, we construct Gibbs measures in infinite volume, as…

Probability · Mathematics 2023-08-21 Héloïse Constantin

We consider the Boltzmann equation with external fields in strictly convex domains with diffuse reflection boundary condition. As long as the normal derivative of external fields satisfy some sign condition on the boundary (1.8) we…

Analysis of PDEs · Mathematics 2019-06-04 Yunbai Cao

Optimal transport (OT) provides effective tools for comparing and mapping probability measures. We propose to leverage the flexibility of neural networks to learn an approximate optimal transport map. More precisely, we present a new and…

Machine Learning · Computer Science 2022-07-06 Florentin Coeurdoux , Nicolas Dobigeon , Pierre Chainais

We introduce a random matrix framework for studying statistical-mechanical lattice systems through spectral observables. Equilibrium configurations sampled from a Boltzmann measure are mapped to matrix ensembles whose covariance structure…

Disordered Systems and Neural Networks · Physics 2026-05-21 Yaprak Önder , Abbas Ali Saberi , Roderich Moessner

We study spectral behavior of sparsely connected random networks under the random matrix framework. Sub-networks without any connection among them form a network having perfect community structure. As connections among the sub-networks are…

Statistical Mechanics · Physics 2015-05-13 Sarika Jalan

Under general assumptions on the target distribution $p^\star$, we establish a sharp Lipschitz regularity theory for flow-matching vector fields and diffusion-model scores, with optimal dependence on time and dimension. As applications, we…

Statistics Theory · Mathematics 2026-04-08 Arthur Stéphanovitch

We introduce a new family of tensorial field theories by coupling different fields in a non-trivial way, with a view towards the investigation of the coupling between matter and gravity in the quantum regime. As a first step, we consider…

High Energy Physics - Theory · Physics 2020-03-11 Vincent Lahoche , Dine Ousmane Samary , Antonio D. Pereira

Normalizing flows have been successfully modeling a complex probability distribution as an invertible transformation of a simple base distribution. However, there are often applications that require more than invertibility. For instance,…

Machine Learning · Computer Science 2023-04-12 Seongmin Hong , Se Young Chun

We show that to understand the orthogonality catastrophe in the half-filled lattice model of spinless fermions with repulsive nearest neighbor interaction and a local impurity in its Luttinger liquid phase one has to take into account (i)…

Strongly Correlated Electrons · Physics 2016-02-04 D. M. Kennes , M. J. Schmidt , D. Hübscher , V. Meden

A general lattice Boltzmann (LB) model is proposed for solving nonlinear partial differential equations with the form $\partial_t \phi+\sum_{k=1}^{m} \alpha_k \partial_x^k \Pi_k (\phi)=0$, where $\alpha_k$ are constant coefficients, and…

Computational Physics · Physics 2018-01-17 Baochang Shi , Hanzhong He , Zhaoli Guo

An invertible function is bi-Lipschitz if both the function and its inverse have bounded Lipschitz constants. Nowadays, most Normalizing Flows are bi-Lipschitz by design or by training to limit numerical errors (among other things). In this…

Machine Learning · Computer Science 2024-03-08 Alexandre Verine , Benjamin Negrevergne , Fabrice Rossi , Yann Chevaleyre