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We prove that coherent configurations can be represented as modules over Frobenius structures in the category of real nonnegative matrices. We generalize the notion of admissible morphism from association schemes to coherent configurations.…

Combinatorics · Mathematics 2025-07-30 Gejza Jenča , Anna Jenčová , Dominik Lachman

Boltzmann Generators have emerged as a promising machine learning tool for generating samples from equilibrium distributions of molecular systems using Normalizing Flows and importance weighting. Recently, Flow Matching has helped speed up…

Machine Learning · Statistics 2025-10-21 Lorenz Vaitl , Leon Klein

A gradient flow equation for $\lambda\phi^{4}$ theory in $D=4$ is formulated. In this scheme the gradient flow equation is written in terms of the renormalized probe variable $\Phi(t,x)$ and renormalized parameters $m^{2}$ and $\lambda$ in…

High Energy Physics - Lattice · Physics 2016-03-23 Kazuo Fujikawa

A simple generative model for rank ordered data with ties is presented. The model is based on ordering geometric latent variables and can be seen as the discrete counterpart of the Plackett-Luce (PL) model, a popular, relatively tractable…

Methodology · Statistics 2022-12-19 Daniel A. Henderson

In this paper, we review state-of-the-art results on the collective behaviors for Lohe type first-order aggregation models. Collective behaviors of classical and quantum many-body systems have received lots of attention from diverse…

Dynamical Systems · Mathematics 2021-08-25 Seung-Yeal Ha , Dohyun Kim

We present a second-order extension of the first-order Lohe hermitian sphere(LHS) model and study its emergent asymptotic dynamics. Our proposed model incorporates an inertial effect as a second-order extension. The inertia term can…

Mathematical Physics · Physics 2020-09-21 Seung-Yeal Ha , Myeongju Kang , Hansol Park

Extracting the maximum amount of cosmological and astrophysical information from upcoming large-scale surveys remains a challenge. This includes evaluating the exact likelihood, parameter inference and generating new diverse synthetic…

Cosmology and Nongalactic Astrophysics · Physics 2022-11-24 Roy Friedman , Sultan Hassan

This thesis presents an overview of the flow equations recently introduced by Wegner. The little known mathematical framework of the flow in the manifold of unitarily equivalent matrices, as discovered in the mathematical literature before…

Nuclear Theory · Physics 2009-09-29 Bruce Henry Bartlett

We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term. The particle density is evaluated from the current…

Machine Learning · Statistics 2022-10-14 Richard D. P. Grumitt , Biwei Dai , Uros Seljak

We study the structure of the Mather and Aubry sets for the family of lagrangians given by the kinetic energy associated to a riemannian metric $ g$ on a closed manifold $ M$. In this case the Euler-Lagrange flow is the geodesic flow of…

Dynamical Systems · Mathematics 2020-05-07 Gonzalo Contreras , José Antônio G. Miranda

This paper introduces Gauge Flow Models, a novel class of Generative Flow Models. These models incorporate a learnable Gauge Field within the Flow Ordinary Differential Equation (ODE). A comprehensive mathematical framework for these…

Machine Learning · Computer Science 2026-03-04 Alexander Strunk , Roland Assam

We present a paradigm for developing arbitrarily high order, linear, unconditionally energy stable numerical algorithms for gradient flow models. We apply the energy quadratization (EQ) technique to reformulate the general gradient flow…

Numerical Analysis · Mathematics 2020-07-15 Yuezheng Gong , Jia Zhao , Qi Wang

In this work, we propose a new model for flow through deformable porous media, where the solid material has two phases with distinct material properties. The two phases of the porous material follow a Cahn-Hilliard type evolution, with…

Mathematical Physics · Physics 2021-09-09 Erlend Storvik , Jakub Wiktor Both , Jan Martin Nordbotten , Florin Adrian Radu

We study emergent dynamics of the Lohe hermitian sphere (LHS) model with the same free flows under the dynamic interplay between state evolution and adaptive couplings. The LHS model is a complex counterpart of the Lohe sphere (LS) model on…

Mathematical Physics · Physics 2021-01-12 Junhyeok Byeon , Seung-Yeal Ha , Hansol Park

Optimal-order uniform-in-time $H^1$-norm error estimates are given for semi- and full discretizations of mean curvature flow of surfaces in arbitrarily high codimension. The proposed and studied numerical method is based on a parabolic…

Numerical Analysis · Mathematics 2022-02-04 Tim Binz , Balázs Kovács

Finding latent structures in data is drawing increasing attention in diverse fields such as image and signal processing, fluid dynamics, and machine learning. In this work we examine the problem of finding the main modes of gradient flows.…

Dynamical Systems · Mathematics 2020-12-29 Ido Cohen , Omri Azencot , Pavel Lifshitz , Guy Gilboa

We propose an interaction flow scheme that sums up the perturbation expansion of many-particle systems by successively increasing the interaction strength. It combines the unbiasedness of renormalization group methods with the simplicity of…

Strongly Correlated Electrons · Physics 2007-05-23 Carsten Honerkamp , Daniel Rohe , Sabine Andergassen , Tilman Enss

The choice of interface conditions for coupling free-flow and porous-medium flow systems is crucial in order to obtain accurate coupled flow models and precise numerical simulation results. Typically, the Stokes equations are considered in…

Analysis of PDEs · Mathematics 2025-07-15 Elissa Eggenweiler , Iryna Rybak

We study a global well-posedness of measure-valued solutions to the kinetic Lohe Hermitian sphere(LHS) model derived from the Lohe tensor(LT) model on the set of rank-1 complex tensors(i.e. complex vectors) with the same size and…

Analysis of PDEs · Mathematics 2021-04-28 Junhyeok Byeon , Seung-Yeal Ha , Gyuyoung Hwang , Hansol Park

Flow fields are often partitioned into data blocks for massively parallel computation and analysis based on blockwise relationships. However, most of the previous techniques only consider the first-order dependencies among blocks, which is…

Machine Learning · Computer Science 2023-12-06 Nan Chen , Zhihong Li , Jun Tao