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The function of biomolecules such as proteins depends on their ability to interconvert between a wide range of structures or "conformations." Researchers have endeavored for decades to develop computational methods to predict the…

Biomolecules · Quantitative Biology 2026-02-05 Daniel D. Richman , Jessica Karaguesian , Carl-Mikael Suomivuori , Ron O. Dror

Class-labeled datasets, particularly those common in scientific domains, are rife with internal structure, yet current class-conditional diffusion models ignore these relationships and implicitly diffuse on all classes in a flat fashion. To…

Machine Learning · Computer Science 2024-02-05 Alex M. Tseng , Max Shen , Tommaso Biancalani , Gabriele Scalia

We study with lattice techniques the localisation of gauge fields on domain wall defects in 2+1 dimensions, following a scenario originally proposed by Dvali and Shifman for 3+1 dimensions, based on confining dynamics in the bulk. We find…

High Energy Physics - Phenomenology · Physics 2009-11-10 M. Laine , H. B. Meyer , K. Rummukainen , M. Shaposhnikov

Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact. In the context of lattice field theory,…

Lattice Boltzmann models provide better understanding with mesoscopic eyesight on multi-component diffusion than macroscopic models. Based on the kinetic theory and starting from the He-Luo model, the state-of-the-art multi-component…

Computational Physics · Physics 2018-09-06 Ju'an Huang , Cheng Bao , Zeyi Jiang , Xinxin Zhang

Strongly coupled gauge theories provide an ultra-violet realization of new physics models for physics beyond the Standard Model of particle physics arising from composite dynamics. Depending on the gauge group and matter content, they are…

High Energy Physics - Lattice · Physics 2024-10-31 Jong-Wan Lee

Recently, a minimal kinetic model for fluid flow, known as entropic lattice Boltzmann method, has been proposed for the simulation of isothermal hydrodynamic flows. At variance with previous Lattice Boltzmann methods, the entropic version…

Statistical Mechanics · Physics 2007-05-23 I. V. Karlin , S. Ansumali , E. DE Angelis , H. C. Öttinger , S. Succi

We derive a fluctuating lattice Boltzmann method for the diffusion equation. The derivation removes several shortcomings of previous derivations for fluctuating lattice Boltzmann methods for hydrodynamic systems. The comparative simplicity…

Computational Physics · Physics 2017-05-15 Alexander J. Wagner , Kyle Strand

The quantum diffusion of a particle in an initially localized state on a cyclic lattice with N sites is studied. Diffusion and reconstruction time are calculated. Strong differences are found for even or odd number of sites and the limit…

Quantum Physics · Physics 2007-05-23 A. C. de la Torre , H. O. Martin , D. Goyeneche

Denoising diffusion models are a novel class of generative algorithms that achieve state-of-the-art performance across a range of domains, including image generation and text-to-image tasks. Building on this success, diffusion models have…

Machine Learning · Computer Science 2024-03-08 Nic Fishman , Leo Klarner , Valentin De Bortoli , Emile Mathieu , Michael Hutchinson

The transfer-matrix of the U(1) lattice model is considered in the Fourier basis and in the weak coupling limit. The issues of Gauss law constraint and gauge invariant states are addressed in the Fourier basis. In particular, it is shown…

High Energy Physics - Lattice · Physics 2022-10-18 Afsaneh Kianfar , Amir H. Fatollahi

An extensive study of the compact $U(1)$ lattice gauge theory with a higher derivative gauge-fixing term and a suitable counter-term has been undertaken to determine the nature of the possible continuum limits for a wide range of the…

High Energy Physics - Lattice · Physics 2017-11-22 Asit K. De , Mugdha Sarkar

We describe the mathematical theory of diffusion and heat transport with a view to including some of the main directions of recent research. The linear heat equation is the basic mathematical model that has been thoroughly studied in the…

Analysis of PDEs · Mathematics 2017-06-27 Juan Luis Vázquez

Generative models such as denoising diffusion models are quickly advancing their ability to approximate highly complex data distributions. They are also increasingly leveraged in scientific machine learning, where samples from the implied…

Machine Learning · Computer Science 2025-03-14 Jan-Hendrik Bastek , WaiChing Sun , Dennis M. Kochmann

We present a concise derivation of the Boltzmann form for single-particle energy distributions in classical many-body Hamiltonian systems. The derivation relies on two physical facts: coarse-graining-scale invariance of the empirical…

Statistical Mechanics · Physics 2026-05-26 Weicheng Fu , Yisen Wang , Yong Zhang , Hong Zhao

We study the deformation and dynamics of droplets in time-dependent flows using 3D numerical simulations of two immiscible fluids based on the lattice Boltzmann model (LBM). Analytical models are available in the literature, which assume…

Fluid Dynamics · Physics 2018-01-17 F. Milan , M. Sbragaglia , L. Biferale , F. Toschi

A unified model of molecular and atomistic spin dynamics is presented enabling simulations both in microcanonical and canonical ensembles without the necessity of additional phenomenological spin damping. Transfer of energy and angular…

We examine the Kogut-Susskind formulation of lattice gauge theories under the light of fermionic and bosonic degrees of freedom that provide a description useful to the development of quantum simulators of gauge invariant models. We…

Quantum Physics · Physics 2015-03-20 Erez Zohar , Michele Burrello

We present a stochastic method for reconstructing missing spatial and velocity data along the trajectories of small objects passively advected by turbulent flows with a wide range of temporal or spatial scales, such as small balloons in the…

Fluid Dynamics · Physics 2024-11-14 Tianyi Li , Luca Biferale , Fabio Bonaccorso , Michele Buzzicotti , Luca Centurioni

Diffusion probabilistic models excel at sampling new images from learned distributions. Originally motivated by drift-diffusion concepts from physics, they apply image perturbations such as noise and blur in a forward process that results…

Image and Video Processing · Electrical Eng. & Systems 2024-06-07 Pascal Peter