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The dynamics of dry active matter have implications for a diverse collection of biological phenomena spanning a range of length and time scales, such as animal flocking, cell tissue dynamics, and swarming of inserts and bacteria. Uniting…

Soft Condensed Matter · Physics 2021-01-07 David Nesbitt , Gunnar Pruessner , Chiu Fan Lee

Efficient sampling of the Boltzmann distribution of molecular systems is a long-standing challenge. Recently, instead of generating long molecular dynamics simulations, generative machine learning methods such as normalizing flows have been…

Machine Learning · Computer Science 2024-08-06 Henrik Schopmans , Pascal Friederich

We present an energy-conserving multiple-relaxation-time finite difference lattice Boltzmann model for compressible flows. This model is based on a 16-discrete-velocity model. The collision step is first calculated in the moment space and…

Statistical Mechanics · Physics 2015-05-14 Feng Chen , Aiguo Xu , Guangcai Zhang , Yingjun Li

Born-rule generative modeling, a central task in quantum machine learning, seeks to learn probability distributions that can be efficiently sampled by measuring complex quantum states. One hope is for quantum models to efficiently capture…

Quantum Physics · Physics 2025-12-03 Mark M. Wilde

We investigate the formation and transport of gas bubbles across a model porous catalyst/electrode using lattice Boltzmann simulations. This approach enables us to systematically examine the influence of a wide range of morphologies, flow…

Soft Condensed Matter · Physics 2024-04-29 Thomas Scheel , Paolo Malgaretti , Jens Harting

We present a novel approach to kinetic theory modeling enabling the simulation of a generic, real gas presented by its corresponding equation of state. The model is based on mass, momentum and energy conservation, and unlike the lattice…

Fluid Dynamics · Physics 2020-02-25 Ehsan Reyhanian , Benedikt Dorschner , Ilya Karlin

In this paper, a generalized lattice Boltzmann (LB) model with a mass source is proposed to solve both incompressible and nearly incompressible Navier-Stokes (N-S) equations. This model can be used to deal with single-phase and two-phase…

Computational Physics · Physics 2019-02-26 Xiaolei Yuan , Zhenhua Chai , Huili Wang , Baochang Shi

A three-dimensional color-fluid lattice Boltzmann model for immiscible two-phase flows is developed in the framework of a three-dimensional 27-velocity (D3Q27) lattice. The collision operator comprises the D3Q27 versions of three…

Fluid Dynamics · Physics 2017-08-02 Shimpei Saito , Yutaka Abe , Kazuya Koyama

In the present study, a consistent and conservative Phase-Field model is developed to study thermo-gas-liquid-solid flows with liquid-solid phase change. The proposed model is derived with the help of the consistency conditions and exactly…

Computational Physics · Physics 2021-11-16 Ziyang Huang , Guang Lin , Arezoo M. Ardekani

For multiscale gas flows, kinetic-continuum hybrid method is usually used to balance the computational accuracy and efficiency. However, the kinetic-continuum coupling is not straightforward since the coupled methods are based on different…

Fluid Dynamics · Physics 2015-05-19 Jianping Meng , Yonghao Zhang , Xiaowen Shan

In this paper, an improved three-dimensional color-gradient lattice Boltzmann (LB) model is proposed for simulating immiscible multiphase flows. Compared with the previous three-dimensional color-gradient LB models, which suffer from the…

Computational Physics · Physics 2019-08-14 Z. X. Wen , Q. Li , Y. Yu , Kai. H. Luo

This work explores the capability of simulating complex fluid flows by directly solving the Boltzmann equation. Due to the high-dimensionality of the governing equation, the substantial computational cost of solving the Boltzmann equation…

Fluid Dynamics · Physics 2023-12-05 Tarik Dzanic , Luigi Martinelli

In this paper, a multiple-relaxation-time lattice Boltzmann (LB) approach is developed for the simulation of three-dimensional (3D) liquid-vapor phase change based on the pseudopotential model. In contrast to some existing 3D thermal LB…

Numerical Analysis · Mathematics 2022-06-03 Jiangxu Huang , Lei Wang , Kun He , Changsheng Huang

A high-performance implementation of a multiphase lattice Boltzmann method based on the conservative Allen-Cahn model supporting high-density ratios and high Reynolds numbers is presented. Metaprogramming techniques are used to generate…

Fluid Dynamics · Physics 2020-12-14 Markus Holzer , Martin Bauer , Ulrich Rüde

Finding reduced models of spatially-distributed chemical reaction networks requires an estimation of which effective dynamics are relevant. We propose a machine learning approach to this coarse graining problem, where a maximum entropy…

Biological Physics · Physics 2018-08-15 Oliver K. Ernst , Thomas Bartol , Terrence Sejnowski , Eric Mjolsness

We extend a recently proposed ternary free energy lattice Boltzmann model with high density contrast, by incorporating wetting boundaries at solid walls. The approaches are based on forcing and geometric schemes, with implementations…

Fluid Dynamics · Physics 2019-07-31 Neeru Bala , Marianna Pepona , Ilya Karlin , Halim Kusumaatmaja , Ciro Semprebon

Simulating inhomogeneous flows with different characteristic scales in different coordinate directions using the collide-and-stream based lattice Boltzmann methods (LBM) can be accomplished efficiently using rectangular lattice grids. We…

Fluid Dynamics · Physics 2021-06-02 Eman Yahia , Kannan Premnath

The lattice Boltzmann equation describes the evolution of the velocity distribution function on a lattice in a manner that macroscopic fluid dynamical behavior is recovered. Although the equation is a derivative of lattice gas automata, it…

comp-gas · Physics 2008-02-03 James D. Sterling , Shiyi Chen

We present a method to achieve reaction-limited evaporation for the color-gradient lattice Boltzmann multicomponent model. Our approach involves a systematic way to remove fluid mass from the interface region in order to achieve evaporation…

Fluid Dynamics · Physics 2024-12-20 Gaurav Nath , Othmane Aouane , Jens Harting

Flows are exact-likelihood generative neural networks that transform samples from a simple prior distribution to the samples of the probability distribution of interest. Boltzmann Generators (BG) combine flows and statistical mechanics to…

Machine Learning · Statistics 2019-10-03 Jonas Köhler , Leon Klein , Frank Noé
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